# Syntasa — Insights (full text)
This file contains the full text of every Insights article published at https://syntasa.com/insights. See https://syntasa.com/llms.txt for a shorter index covering the rest of the site.
# A New Way to Talk to Enterprise Data: Syntasa Customer Insights Agent on Gemini Enterprise
URL: https://syntasa.com/insights/customer-insights-agent-gemini-enterprise
Date: 2026-10-07
Category: POV
Audience: Enterprise
Getting an answer from data still requires knowing a lot about the data. The Customer Insights Agent, now on Gemini Enterprise, lets people ask questions the way they think about them, and keep going.
For decades, organizations have invested enormously in making more data available. We moved from operational databases to data warehouses, from warehouses to cloud data platforms, and increasingly to sophisticated analytics infrastructure.
But one thing hasn't fundamentally changed: getting an answer from data still requires knowing quite a lot about the data.
A business user might ask something perfectly reasonable: Why did our conversion drop last month? Who are our highest-value customers? Which marketing channels are driving engagement?
Answering these questions often requires knowing which datasets contain the relevant information, how the organization defines key terms, what filters should be applied, and sometimes whether the observed pattern is even statistically meaningful. Traditionally, someone who deeply understands both the data and the business sits between the question and the answer. Often, that person is busy, and the cycle is slow.
Generative AI gives us an opportunity to change that.
## From translation to conversation
Natural language to SQL has existed in various forms for years, and LLMs have made it dramatically better. But generating SQL isn't the same as understanding a business question.
Consider the simple question: Who are our best customers?
There is no universally correct query for that. Does "best" mean highest lifetime value? Most frequent purchases? Highest predicted future value? Most engaged? Should returns be factored in? What about customers who haven't purchased recently?
An experienced analyst doesn't immediately write SQL. They interpret the question using what they know about the business, its priorities, and its data. They think about context.
That's what we set out to build into the [Customer Insights Agent](/enterprise/gemini-enterprise-customer-insights-agent/). The goal isn't simply to translate English into SQL. It's to enable agents that understand enough about an organization's data and business definitions that people can ask questions the way they naturally think about them—and keep going.
Why did conversion drop? Was it the same for new and returning customers? Which channels changed the most? Is that happening across every product category?
Analysis is rarely one question followed by one answer. It's iterative. One observation creates the next question. A useful data agent needs to work the same way.
## How agents learn your business
LLMs are remarkably good at reasoning, but they arrive with no knowledge of what your organization's data actually means.
Every enterprise has its own vocabulary. A "customer" in one system may mean something entirely different in another. Revenue can be defined multiple ways. Marketing channels are classified differently across teams. A metric that sounds obvious might depend on years of institutional knowledge scattered across data models, dashboards, SQL, and people's heads.
Rather than asking organizations to hand-craft comprehensive ontologies upfront, we built a learning harness into the Customer Insights Agent. Every time you validate an answer, that becomes a reference point. When you flag something as incorrect, we capture that feedback. Over time, the agent builds understanding from real usage patterns and validated examples from your business.
This is how the agent self-evolves. It's not magic. It's structured learning—your team continuously teaches the agent what right looks like in your business, and the harness ensures that learning compounds. The agent becomes smarter the more you use it.
## Trust means showing the work
There's a fundamental difference between asking an AI a general knowledge question and asking it something that might influence a business decision.
The answer has to be verifiable.
Business users shouldn't need to understand your data model to ask a question. But your analysts and data teams absolutely need to see what happened behind the scenes. What data was queried? How was the question interpreted? What exactly was executed? How was the answer derived?
This is why the Customer Insights Agent shows you everything. The SQL. The tables accessed. The logic. The confidence level. Not because it's a nice feature, but because trust in AI-driven insights requires transparency. Your data team can validate it. Your business users can act with confidence. Everyone can see the working.
## Keep data where it belongs
Organizations have spent years building governance around their cloud data environments—permissions, encryption, auditing, data quality policies, access controls. That infrastructure works. It's maintained. It's audited.
An AI agent shouldn't require you to copy that data to an application layer and rebuild all of those protections again. That's maintaining two systems. They drift. One gets updated, the other doesn't.
The Customer Insights Agent is designed differently. It pushes execution down to your underlying data platform. Data stays where your organization already manages it. Your BigQuery access policies, your audit logs, your encryption—they all remain in effect. The agent inherits your governance automatically. There's no second layer to maintain.
This becomes even more critical as agents move from answering questions to taking action. Today you ask: Why did conversion fall? Tomorrow you might ask: Find the customers most affected and create an audience for analysis. The closer agents get to action, the more governance and transparency matter.
## Built with Google
We couldn't have built this without Google's partnership. The Gemini models—particularly the reasoning capabilities in Gemini Pro—made it possible to create an agent that handles complex multi-step business reasoning reliably. Google's team was instrumental in helping us bridge the zero-copy gap and push the boundaries of agent-to-agent orchestration, making it possible for the Customer Insights Agent to work seamlessly as part of a broader multi-agent system.
Having the Customer Insights Agent available natively on Gemini Enterprise means your team accesses it in the same place they're already using Gemini for other tasks. It's not a separate tool. It's an extension of your AI assistant, purpose-built for your data warehouse.
## Available now on Gemini Enterprise
Today, we're announcing that the Customer Insights Agent is available on Gemini Enterprise through [Google Cloud Marketplace](https://console.cloud.google.com/marketplace/product/syntasa-public/syntasa-customer-insight-agent-saas).
For users, the interaction is intentionally simple: ask a question. Behind that question is something much richer—your business context, your enterprise data, your existing governance, and the analytical rigor required to turn a question into an answer you can actually use.
You can start with a free trial to see how it works with your data. Connect your BigQuery tables, set up your business context, and let your teams experience what it means to have a conversation with your data instead of waiting for a data engineer.
## The opportunity ahead
For years, the focus has been on giving more people access to data.
Perhaps the more interesting challenge now is giving more people the ability to question it. To explore. To iterate. To uncover insights that matter to their business without getting stuck waiting for technical resources.
The Customer Insights Agent, built with Google's Gemini and available on Gemini Enterprise, is our answer to that challenge. It's designed for organizations that want to adopt AI for customer intelligence but need to do it with trust—with transparency, security, and governance built in, not bolted on afterward.
We're excited to see what your teams discover.
**Ready to have a conversation with your own data?** Request a free trial of the Customer Insights Agent on Gemini Enterprise, connect your BigQuery tables, and see what your teams ask first.
---
# The Guardrails Are in the Data: Why AI Safety Starts Beneath the Model
URL: https://syntasa.com/insights/guardrails-are-in-the-data-ai-safety
Date: 2026-09-30
Category: POV
Audience: Mission
Most of the AI safety debate is about the model. But once agents start acting on their own, the risk lives underneath it, in the data they read and the systems they can touch.
## We are guarding the wrong door
Most of the AI safety debate is about the model: how it was trained, what it will refuse, how it scores on the latest benchmark. Those questions matter. But in the places where AI is now being put to work, they are no longer where the risk lives.
The risk lives underneath the model, in the data it reads and the systems it can touch. A well-aligned model that is fed the wrong data, or handed the keys to systems it should never reach, is not safe. It is just polite while it makes a mistake.
## Agents act, and actions have consequences
For the first wave of generative AI, the worst case was a bad answer. A person read it, frowned, and moved on. Agentic AI removes that person from the loop by design. Agents query databases, trigger workflows, draft and send, flag and escalate. They do it at machine speed and at a scale no reviewer can match.
That changes the safety question. It is no longer only "what will the model say?" It is "what data can this agent see, what is it allowed to do, and can we prove afterwards what it did and why?" Those are not model questions. They are questions about data, identity, permissions and audit, and no amount of fine-tuning answers them.
## Safety is a platform property
My argument is simple: in the agentic era, AI safety is mostly a data and platform problem. A safe agent is one that runs on a foundation with five properties.
- **Custody.** The organization keeps its data, its models and its model weights. Nothing leaves the boundary unless someone decided it should.
- **Governance before intelligence.** Data is prepared, labeled and governed before any model touches it, so agents reason over facts the organization actually trusts.
- **Least privilege.** Every agent acts under a bound identity with only the access its task requires, the same zero-trust discipline we already demand of people.
- **Traceability and auditability.** Every query, every movement of data and every action is logged, so an agent's decision can be reconstructed and challenged.
- **No hidden dependencies.** No quiet calls to outside APIs, no telemetry phoning home. If you cannot see the dependency, you cannot govern it.
None of this is glamorous. All of it is what separates an AI system you can defend in front of an auditor, a regulator or a mission commander from one you merely hope will behave.
## What this looks like in practice
At Syntasa, we built our [data and agentic AI platform](/platform) on the premise that you bring AI to the data, not the data to the AI. The platform runs inside the customer's own boundary, on their BigQuery, S3, Snowflake or on-premises estate. Data is never copied into a Syntasa-owned store. The customer keeps the data, the models and the boundary; we hold none of it.
The same codebase covers ingestion and identity, governance and orchestration, model training and serving, agentic workflows and activation. That matters for safety. Agents are not bolted onto an ungoverned data lake after the fact. They inherit the governance, access controls and audit trail of the pipeline that feeds them.
The proof is in where it runs. The platform is [FedRAMP High authorized](/trust-center), holds live federal ATOs and is [certified on Google Distributed Cloud](/insights/syntasa-google-distributed-cloud) for air-gapped use. At one U.S. federal agency it went from air gap to operational use in six weeks, with no external dependencies and no data leaving the enclave. The same product, from the same code, serves commercial customers such as Lenovo, Sky and Currys at petabyte scale.
That last point is the one I would underline. Safety that only exists in the most classified environments is a niche. Safety that is the default architecture, from a retailer's website to a classified network, is a standard.
## Where I stand
I will be direct about my own view. I do not think AI safety will be won by slowing down, and I do not think it will be won by a handful of labs getting the model perfectly right. It will be won, or lost, in thousands of ordinary deployment decisions made by people who run data platforms.
I am wary of the pattern I see across the market, where the price of using AI is handing your data to someone else's store. Lock-in is not just a commercial problem. When your data sits in a system you do not control, so does your ability to explain what your AI did with it. Sovereignty is a safety feature.
I also think our industry talks about safety in adjectives when it should talk in evidence. "Trusted," "secure" and "responsible" are easy to print. Authorizations, audit logs, deployment timelines and measured outcomes are harder to fake. Buyers should demand the second kind, and vendors, including us, should expect to be held to it.
Finally, I believe safety and value are not a trade-off. The organizations that govern their data properly are the ones that can deploy agents fastest, because they are not relitigating trust on every project. The guardrails are what let you drive.
## Four questions before the next agent ships
For any leader about to put agents into production, I would ask four questions before asking which model to use:
1. Where does our data live while the agent works, and who else can see it?
2. What exactly is this agent permitted to read and to do, and who approved that?
3. If it gets something wrong, can we reconstruct every step it took?
4. Does anything in this stack call out to a system we do not control?
If the answers are vague, the model is not your problem yet. Fix the foundation first. The next chapter of AI safety will not be written in research papers alone. It will be written in data platforms, one governed deployment at a time.
**Want to test your own foundation against those four questions?** Our team can walk through them with you, whether your agents run in a commercial cloud or on an air-gapped network.
---
# How Google's UCP and warehouse-native context power the future of agentic shopping
URL: https://syntasa.com/insights/google-ucp-warehouse-native-context-agentic-shopping
Date: 2026-09-22
Category: POV
Audience: Enterprise
The Universal Commerce Protocol has made agent readiness legible for the first time. What it hasn't done is make your data ready — that work still sits in your warehouse.
Commerce has changed where it starts before: store to browser, browser to phone, phone to feed. It is happening again, and this time the shopper is not the one doing the looking. An AI agent is. It reads a question, queries whatever product data it can reach, and returns three or four options. Adobe Analytics put traffic to US retail sites from generative AI tools up 693.4% year over year across the 2025 holiday season. On Shopify's fourth quarter earnings call in February, president Harley Finkelstein said orders reaching Shopify stores from AI search were up fifteenfold since January 2025, off what he was careful to call a small base. McKinsey puts the destination at $3 trillion to $5 trillion of global consumer commerce mediated by AI agents by 2030.
What makes this shift different from the last three is that it has a protocol, which means for the first time you can know precisely whether your business is ready. It also means the hard part is now visible. A protocol defines the shape of the answer. It does not produce the answer. That work sits in your data, and it is the difference between being one of the three or four brands an agent names and being one it never reaches.
## What is UCP, and why does it matter now?
The Universal Commerce Protocol is an open standard for how commerce data and transactions move between merchants and AI agents. Google and Shopify announced it at NRF on 11 January 2026, and it does for agentic shopping roughly what a shared file format does for documents: it removes the need to build a bespoke integration for every surface a customer might shop on.
Two things about UCP are worth knowing before you plan around it.
**It's moving fast, and its versions are dated, not numbered.** [The releases](https://github.com/Universal-Commerce-Protocol/ucp/releases) so far are v2026-01-11, v2026-01-23, v2026-04-08 and v2026-08-25. There is no "v0.8," and secondary trackers frequently invent one. The April 2026 release is the one that mattered most for functionality: it added a cart capability for basket building, catalog search and lookup, request and response signing, [eligibility claims and a verification contract](/insights/built-to-rank-or-built-to-be-chosen), a formalized totals contract, discount handling, order operations with authentication, and OAuth 2.0 identity linking. The August 2026 release mattered more for structure: it reorganized the protocol so it could expand into new verticals beyond retail, strengthened payment security, and let each capability be versioned on its own instead of all at once.
**The field has kept consolidating around it, month after month, since the April Tech Council expansion.** On 24 April 2026, Amazon, Meta, Microsoft, Salesforce and Stripe joined the UCP Tech Council, alongside founding participants Google and Shopify. On 16 July 2026, a Food Technical Council formed with Block, DoorDash, Google, Toast and Uber Eats. On 11 August 2026, a Lodging Technical Council formed with Amadeus, Booking.com, Expedia, Google, Hilton, Marriott and Trip.com. On 2 September 2026, a Payments Technical Council formed with Adyen, Ant International, Coinbase, Global Payments, Google, PayPal, Shopify and Stripe. Google is a member of every one of these councils. When the companies that own the demand surfaces, the storefronts, the booking engines and the payment rails all standardize on one specification, the specification stops being a bet and starts being a requirement.
Read the release list again and notice what it is actually asking for. Catalog search and lookup assumes a catalog an agent can query, not a feed it can crawl. Eligibility claims assume you can state, per shopper and per item, whether an offer applies. A totals contract assumes your pricing, tax, shipping and discount logic can be resolved on demand and defended. None of that is a document you publish. All of it is data you have to be able to produce, live, on request.
## Why is the protocol the easy part?
Because a protocol defines the shape of the answer. It does not produce the answer.
This is the part most readiness conversations skip. A brand adopts UCP, connects a surface, and discovers that the agent is now asking questions its own systems have never had to answer in one place. Catalog lives in a PIM. Reviews live with a third-party provider. Inventory lives in an ERP. Pricing and promotions live in a merchandising tool, and the rules that govern them often live in someone's head. Customer data is split across a warehouse, an ESP and an analytics stack.
For twenty years that fragmentation was survivable, because the interface was a page. A page can be assembled slowly, from many sources, and a shopper will wait a second for it. An agent will not. It asks a specific question, takes whatever comes back within its budget, and cites the brands whose answers were usable.
[Your catalog gets you found on Google. It will not get you chosen by agents.](/insights/product-catalog-vs-shopping-agent-signals) Catalog data optimized for keyword match is built to rank. Agent-ready data is built to be chosen, which is a different specification entirely: structured, queryable, current, and complete enough that a machine can defend a recommendation.
The stakes are simple arithmetic. AI answers surface three to four brands per query. Queries in AI mode run two to three times longer than traditional search queries, which means they carry more constraints, which means more brands get filtered out on details their data could not supply. There is no page two to be on.
## What does a context layer actually do?
Scott Brinker's framing in the 2026 State of Martech is the clearest available. He distinguishes the customer's context, the company's context, and systems context, meaning the subset of the first two that is actually visible and actionable inside your systems. For most businesses only a fraction of what they know ever reaches systems context. Closing that gap, he argues, is the work.
That gap is exactly what Syntasa closes, and it does two jobs to close it.
On the product side, it [merges and restructures catalog, reviews, inventory, pricing and offers into structured output](/insights/one-product-every-channel-ai-agents) that any surface can consume: an AI agent, a UCP endpoint, a Merchant Center feed, on-site search, a partner marketplace, the brand's own storefront. That is what makes a brand appear, accurately, when someone asks an assistant for a recommendation.
On the customer side, it unifies first-party customer data so [the brand's own shopping agent knows who it's talking to](/enterprise/agentic-marketing-platform). Aaiz Khan, Syntasa's Product Marketer, describes the requirement through a salesperson: the great one knows the products cold and remembers the customer. An agent needs both, and most brands' data can give it neither.
All of this runs inside the brand's [existing data warehouse](/insights/data-warehouse-marketing-powerhouse), on AWS, Azure, Snowflake or Google Cloud. That distinction matters more than it sounds. Warehouse-native is not warehouse-locked. There is no new system to buy, no copy of the customer file leaving the perimeter, and no dependency on having picked the right platform vendor two years ago.
## One layer, every surface
The reason to build context once, in the warehouse, is that the surfaces will keep multiplying.
Today it is Google's AI Mode and Gemini, ChatGPT, Claude, dynamic feeds and the brand's own on-site agent. UCP is what makes those surfaces addressable through one specification instead of six integrations. But UCP still has to be fed, and the brand that has built its product and customer context once can serve every one of those surfaces from the same source. The brand that builds per channel is committing to rebuilding for whichever surface arrives next.
## Discover in AI, buy on site
One correction to the prevailing story, because it changes where the investment should go.
In-agent checkout has not become the default anyone expected. OpenAI withdrew Instant Checkout in March 2026, about five months after launch, after Walmart found that in-ChatGPT purchases converted at roughly a third the rate of shoppers who clicked through to Walmart.com. Shopify moved to Agentic Storefronts. Google's agentic checkout remains US-only, even though AI Mode itself is live in the UK and more than forty European markets.
The pattern that is working is discovery in AI and conversion on the brand's own site, with a capable agent waiting there. Macy's is the public example: their Gemini-powered "Ask Macy's" assistant, on their own site, produced roughly 375% higher spending and revenue per visit 4.75 times higher among customers who used it during beta.
That is the same context layer doing both jobs. Product context gets you cited by the agent. Customer context makes your own agent worth arriving at.
## What this means for your business
UCP has made agent readiness legible. The specification is public, the release notes are dated, and the questions an agent will ask your systems are now written down.
What it has not done is make the data ready. That work sits where it always did, in your warehouse, and it is the difference between being one of the three or four brands an agent names and being one of the ones it never reaches.
Syntasa's Agentic Commerce Readiness Check is a short diagnostic of where your product and customer data stand against what agents are already asking for.
[**Take the Agentic Commerce Readiness Check →**](/resources/agentic-commerce-readiness-check)
---
# Syntasa Launches Customer Insights Agent, Built with Gemini, on Google Cloud Marketplace
URL: https://syntasa.com/insights/customer-insights-agent-google-cloud-marketplace-launch
Date: 2026-09-22
Category: NEWS
Audience: Enterprise
Now available with a free trial for early users, Customer Insights Agent turns plain-English questions into insights teams can act on, without waiting on a data team.
Syntasa today announced that its Customer Insights Agent, built with Gemini, is now available on the Google Cloud Marketplace, with a free trial open to early users.
## What It Does
Customer Insights Agent lets marketers, product owners, and CX teams ask customer data questions in plain language and get answers they can act on, without waiting on a data team.
## How It Works
The agent works directly from your data warehouse, paired with a state-of-the-art intelligence layer that understands your business context and surfaces insights relevant to your situation, rather than generic, contextless answers.
## Why Teams Need It
Teams use Customer Insights Agent to spot indicators like churn risk early, identify high-value segments, measure campaign performance, and build targeted audiences, across CRM, commerce, loyalty, marketing, and engagement data.
## Built to Be Trusted, Not Just Fast
> "While tech has evolved to give you information fast, I believe innovation is building something you can trust. It works directly from your data warehouse, the source of all truth, and we deploy a state-of-the-art intelligence layer that understands your business context and provides insights that are relevant to your situation," said Jay Marwaha, Founder and CEO at Syntasa.
## Ready to Get Started?
Customer Insights Agent is live now on the Google Cloud Marketplace. Organizations already using Google Cloud can deploy directly from the listing, start a free trial, or [connect with our team](/request-a-demo) to learn more.
## Explore More
- [Query Customer Data with The Customer Insights Agent](/resources/customer-insights-agent-ask-questions-get-answers)
---
# Built to rank or built to be chosen: why your SEO playbook doesn't transfer to AI agents
URL: https://syntasa.com/insights/built-to-rank-or-built-to-be-chosen
Date: 2026-08-25
Category: POV
Audience: Enterprise
AI assistants no longer hand shoppers a ranked list — they choose three or four brands and stop. Winning a place in that shortlist depends on product data an agent can trust, not the SEO tactics built for human readers.
Ask an AI assistant for a product recommendation and you get three or four brands.
Not thirty. Not a ranked list you can scroll. Three or four, selected on the shopper's behalf and presented as an answer rather than a set of options.
That is the part worth sitting with, because it changes the shape of the problem. Around 35% of shoppers now use AI tools at the discovery stage, against 13.6% on traditional search. When they do, the assistant surfaces a handful of brands and stops. The ones outside that handful are not ranked lower. They are absent, and there is no page two to climb to.
So the question stops being *how do we rank higher* and becomes something less familiar: **can an agent read our data well enough to put us in the answer at all?**
Most brands are still answering the first question. Their catalog was built for it.
## What SEO was actually optimizing for
It is worth being precise about what the old playbook assumed, because the assumptions are what broke, not the tactics.
Search engine optimization rested on four premises. There is a *page*. A *human* reads it. That human sees a *ranked list*. And they *click*.
Everything downstream followed. Keyword-matched titles and descriptions, because a person scans for recognizable words. Backlinks, because authority had to be inferred from who pointed at you. Meta descriptions, because you were writing a pitch for a results page. Page speed, because a human abandons a slow load.
None of it was wrong. It was a rational response to how discovery worked, and it worked for two decades.
But every one of those premises assumed a reader who browses, compares and decides. An agent does none of those things.
## What an agent needs instead
An agent does not browse. It queries.
It does not form an impression of your brand from your homepage. It asks structured questions and takes structured answers. Where an answer is missing, slow or internally inconsistent, it moves on to a brand that answered cleanly. There is no equivalent of a shopper giving you the benefit of the doubt because the photography looked good.
Three things it needs, none of which are content problems.
- **Attributes it can parse.** Not "breathable summer fabric" buried in a description, but material, weight, fit and season as discrete, queryable fields. A person infers. An agent needs it declared.
- **Availability it can trust.** Real-time, at variant level. An agent that recommends an item you sold out of yesterday has damaged the customer's trust and yours in the same sentence. And it will learn not to do that again.
- **Eligibility it can check.** Whether this offer applies to this customer, in this market, at this moment. The April 2026 release of the Universal Commerce Protocol added precisely this: eligibility claims and a verification contract, alongside catalog search and lookup capabilities for product discovery. Read the specification and the direction is unambiguous. Agents are being given the ability to ask exact questions, which means the answers now have to exist.
And critically, an agent cannot be persuaded. It has no aspiration to be seen carrying your bag. Copy that would move a person moves nothing here. What moves an agent is whether the data supports the recommendation.
## SEO vs AI search: four contrasts that matter
The shift is easiest to see side by side.
**Keywords become attributes.** SEO rewarded the right words in the right places. Agent retrieval rewards structured, queryable fields. The same product needs a different description of itself.
**Page rank becomes citation share.** Position on a results page was the old scoreboard. The new one is how often you appear in the answer, across assistants, for the queries that matter to your category. It is a harder number to see, which is exactly why most brands are not measuring it yet.
**Traffic becomes being shortlisted.** Traffic was the goal because traffic converted. Now the decisive moment happens before any traffic exists, in the shortlist the agent assembles. You can lose without ever seeing a visit, and without anything in your analytics telling you why.
**Content freshness becomes data freshness.** Publishing cadence used to be the freshness signal. Now it is whether your inventory, pricing and offers are current *at the moment of the query*. A blog updated weekly and a stock feed updated overnight are no longer the same kind of stale.
## Why this is a data project, not a content project
Here is the uncomfortable part, and the reason this transition stalls inside most organizations.
None of those four contrasts can be closed by a content team.
Attribute completeness lives in the product information management system. Real-time availability lives in inventory. Eligibility lives in pricing and promotions logic. Reviews and sentiment, broken out by attribute rather than averaged into a star rating, live in a review platform that was never designed to be queried that way.
All of it exists. Almost none of it exists in one place, in a form anything can query within the time an agent is willing to wait.
Scott Brinker has a useful name for this gap. He describes three kinds of context: the customer's context, the company's context, and *systems* context, meaning the subset of the first two that is actually visible and actionable in your systems. Where all three align, he calls it Golden Context. His observation is that for most businesses, only a fraction of the first two ever reaches the third.
That is the work. It is not a rebrand or a new tone of voice. It is the unglamorous business of getting data the company already owns into a state where software can use it. Which is why it tends to sit unowned, somewhere between the marketing team that feels the symptom and the data team that holds the fix.
One more thing worth naming, because it determines where the effort should go. In-agent checkout has not become the norm many predicted. OpenAI withdrew Instant Checkout in March 2026, roughly five months after launching it, after Walmart found that purchases completed inside ChatGPT converted at about a third the rate of click-throughs to Walmart.com. Shopify moved in the same direction. Google's agentic checkout remains US-only.
The pattern that has actually formed is this: **discover in the assistant, buy on the brand's own site.**
Which is better news than it sounds, because it means both decisive moments belong to you. Product data good enough for an agent to find you and describe you accurately. And an agent on your own site capable enough to convert once the shopper arrives. Neither is something a platform will hand over.
## The question worth sitting with
If an AI agent read your product catalog this afternoon, what would it be unable to tell a customer?
Most catalogs can supply a title, a description, a price and a category. Fewer can say whether an item is in stock in the right size, right now. Or which products are genuinely substitutable. Or what reviewers actually say about fit, as opposed to an average star rating.
That gap is the difference between being recommended and being skipped. And unlike a ranking problem, you cannot see it from the outside. Nothing tells you which shortlists you did not make.
**Being chosen starts with knowing what an agent can actually see.**
The Agentic Commerce Readiness Check evaluates five of the capabilities that determine whether AI shopping agents can find, understand and act on your product data. In about five minutes, you'll see where your commerce stack is ready, where context is missing and what to address first.
[**Take the Agentic Commerce Readiness Check →**](https://go.syntasa.com/agentic-commerce)
---
# AHCCCS Launches Provider Locator Agent to Improve Access to Opioid Use Disorder Treatment Across Arizona
URL: https://syntasa.com/insights/ahcccs-provider-locator-agent
Date: 2026-07-14
Category: MENTIONS
Audience: Mission
AHCCCS, in collaboration with Google Public Sector and Syntasa, launched the Provider Locator Agent — a multilingual chatbot built on Google's Gemini models that helps Arizonans find substance use treatment services in real time.
Originally published by the Arizona Health Care Cost Containment System (AHCCCS).
Source: https://www.azahcccs.gov/shared/News/GeneralNews/GenAIPoweredProviderLocator.html
---
# Your Product Catalog Gets You Found on Google. It Won't Get You Chosen by a Shopping Agent.
URL: https://syntasa.com/insights/product-catalog-vs-shopping-agent-signals
Date: 2026-06-19
Category: POV
Audience: Enterprise
The catalog your team spent years optimizing was built to rank on a search page. Here's what structured product intelligence looks like and why agents make purchase decisions on entirely different signals.
Your catalog ranks. You know it does. The images are optimized, the GTINs are clean, the descriptions hit the right keywords, the feed quality scores are green. You've put real years into this infrastructure.
But a shopping agent doesn't browse your catalog. It reasons over it.
The query reaching your product data isn't a human typing "waterproof running shoes women size 8" into a search bar. It's an agent with a user preference model, a budget, a set of inferred priorities, and a task to complete. That agent won't click your product page and read the description. It will query your product data, extract the attributes it can reason over, compare them against a dozen other signals, and make a recommendation in seconds.
If your catalog was built to win a search ranking, you optimized for the wrong moment.
Already, 37% of Google searches trigger an AI-assisted result, and products surfaced through agentic recommendations convert at 14.2% compared to 2.8% for standard search. The channel is real, and it rewards different inputs.
## What Your Catalog Was Actually Built to Do
For fifteen years, the game was simple: match the query, win the impression, earn the click.
A product catalog optimized for search engines is a structured inventory designed to match keyword queries and earn page-one placement through attribute completeness, relevance signals, and technical compliance.
The whole architecture assumed a human at one end of the transaction. A person who would see your product, form a judgment, and click through to your site. Approximately 45% of Google searches now surface an AI Overview, shifting first-touch product discovery away from that traditional click-through model. And that number doesn't account for the growing share of shopping intent going through ChatGPT, Perplexity, Gemini, and autonomous shopping agents that handle the entire purchase on a user's behalf.
Google has responded by adding dozens of new conversational attributes to Merchant Center, fields like question_and_answer, related_product, and popularity_rank, designed to feed its Gemini and AI Mode surfaces. Most brands haven't touched them yet. And even those that have are only optimizing for one agent ecosystem out of several that now have real purchase volume.
Your catalog was the right answer for the last era. The rules have changed. Search engines matched queries to content. Agents match intent to outcomes, and the signals that drive that match have nothing to do with keyword density or feed quality scores.
## How Shopping Agents Actually Make Decisions
An AI shopping agent is a software system that receives a user's purchase intent, queries available product sources, reasons across options against that user's preferences and context, and either returns a ranked recommendation or completes the transaction autonomously. It does not browse. It does not click. It infers.
When a human browses your product page, they do interpretive work in real time. They read your description, weigh the review score, notice the return policy, and form a judgment from a mix of explicit information and ambient signals.
An AI shopping agent queries structured data, extracts attributes, and runs inference against a user's context model. If the attribute it needs isn't present in a structured, extractable form, it either fills the gap with a guess or moves on to a product where the signal is clearer.
The signals a shopping agent evaluates look different from the signals a search engine ranks:
The paragraph your merchandising team wrote about why this jacket is perfect for the active lifestyle? An agent doesn't weigh it. It needs structured, extractable facts it can compare across options. This creates what we call the recommendation gap: products that rank well in traditional search but lose at the agent selection layer because they lack the reasoning-ready signals agents need to choose them confidently.
## What Structured Product Intelligence Actually Means
Structured product intelligence is the enrichment layer that transforms raw catalog attributes into agent-readable, context-aware signals that power confident product recommendations at the moment of agent query.
Your existing catalog has the raw material. The problem is that it exists in a form optimized for human interpretation and search-engine indexing, not for agent inference. Building a product intelligence layer means enriching that raw data along four dimensions:
- **Contextual attribute depth.** Beyond the spec sheet. An agent helping someone find a laptop for a college student needs to know more than screen size and RAM. Is this model well-reviewed for durability? How does battery life hold up under real-world conditions? These require enrichment derived from review data and behavioral signals, structured in a form the agent can query.
- **Dynamic trust signals.** Search engines index your review score. An agent needs something more nuanced: a distilled sentiment signal it can reason over. "4.3 stars on 2,400 reviews with strong sentiment around durability and weak sentiment around battery life" is a reasoning-ready trust signal. "4.3 stars" is a number.
- **Intent-matching metadata.** Traditional catalog metadata maps to keywords. Agent-ready metadata maps to user intents. A user intent like "I need a gift for a runner who already has gear but wants something small" can't be resolved by keyword matching. It requires product attributes structured around use cases, recipient profiles, and occasion fits.
- **Decision-support summaries.** An agent that has to parse five paragraphs of marketing copy will either skip it or hallucinate. A clean, structured summary such as "Best for: moderate hikers prioritizing weight over waterproofing. Not ideal for: multi-day trips in wet conditions" gives the agent the signal it needs to make a confident recommendation.
- **Brand rules and recommendation boundaries.** An intelligence layer doesn't just tell an agent what to recommend. It tells the agent what not to. Which products are excluded from promotional positioning, which categories carry margin constraints, which items should never be bundled together. Without this layer, agents operate without guardrails, surfacing recommendations that are technically accurate but commercially wrong. Brand rules are what make agentic commerce safe to scale.
## The Gap and What to Do About It
Most enterprise brands are 80 to 90 percent of the way there on raw data. The transactional history, the behavioral signals, the product attributes, the review data — it exists. The gap is the intelligence layer that takes that data and makes it usable by agents at the moment of query.
Three signs your catalog isn't agent-ready:
- Your products appear in search results but rarely surface in AI Overview product carousels.
- You have no structured signal for "why this product for this buyer right now."
- Your product data lives in BigQuery but isn't flowing into anything that powers real-time agent queries.
The competitive risk compounds. Brands that build a product intelligence layer now will hold recommendation share for the same reason early SEO movers held rankings: more agent interactions feed back into a better intelligence layer, which generates more recommendations, which generates more data. The flywheel favors whoever starts first. Every agent transaction generates preference data that sharpens the next recommendation. Brands with six months of that data will outperform brands starting from zero, not because they have better products, but because their intelligence layer has learned what their agents' buyers actually choose.
Building this layer doesn't require replacing your catalog infrastructure. It requires building on top of it. That's the gap Syntasa's [Agentic Marketing Platform](/enterprise) is built to close, sitting on your existing Google Cloud infrastructure, enriching and activating your product data for the agentic commerce layer.
The brands that close this gap first will be chosen. The brands that don't will keep ranking.
Structured product intelligence is the enrichment and activation layer that makes catalog data usable by AI shopping agents. It goes beyond SEO-optimized attributes to include contextual fit scoring, intent-mapped metadata, trust signal synthesis, and real-time decision-support summaries. Brands that build this layer are positioned to be chosen by agents. Those that don't will rank, but lose at the moment that matters.
Syntasa's [Agentic Marketing Platform](/enterprise) builds and activates product intelligence that works across every agent surface, Google's AI Mode and Gemini, ChatGPT Shopping, Perplexity, and the autonomous agents handling purchase decisions on your customers' behalf. It runs on your existing Google Cloud stack, but the intelligence it activates isn't limited to one ecosystem. [Book a demo](/#brief) to talk through your specific infrastructure.
---
# Syntasa 9.1.0 Now Available
URL: https://syntasa.com/insights/syntasa-9-1-0-now-available
Date: 2026-06-08
Category: NEWS
Audience: Mission, Enterprise
McLean, VA – June 8, 2026 – We're pleased to announce the general availability of Syntasa 9.1, our most comprehensive release to date, delivering major improvements across notebook collaboration, cloud security, AI-powered assistance, job execution, analytics, and platform administration.
## Notebook Workspaces Reimagined
Syntasa 9.1 introduces a completely restructured notebook workspace model. The legacy shared/private folder hierarchy has been replaced with a unified flat workspace, making notebooks and resources easier to organize and discover. Every user now receives a personal workspace automatically on first login, alongside shared group workspaces for team collaboration.
Real-time collaboration (RTC) is now built into every notebook by default. Multiple users can edit simultaneously, with live cursors, presence indicators, and shared cell output, enabling true pair programming and joint debugging without any configuration.
Additional notebook improvements include a new JupyterLab Runtime Toolbar for managing Spark compute resources directly within the notebook interface, per-notebook initialization scripts stored inside the .ipynb file for full portability, a unified utility library (synutils) providing a single consistent API across Python and Scala for credentials, data access, cloud storage, and more, and native SQL magic (%sql / %%sql) support in both Python and Scala kernels.
## Enterprise Security, Enforced at Every Layer
Security is a central theme of this release. Syntasa 9.1 introduces Data Plane Access Control, which uses AWS IAM Session Policies and GCP scoped tokens to dynamically restrict every Spark and notebook session to only the data a user is authorized to access. Scheduled jobs now execute under the job owner's identity rather than a broad system account.
Cross-GCP Project Support allows organizations to decouple the platform's Control Plane from its Data Plane across separate GCP projects, enabling better billing separation, compliance isolation, and a foundation for multi-workspace architectures.
Per-Group Service Accounts let teams bring their own cloud identities (AWS IAM Roles, GCP Service Accounts, or Azure Service Principals) attached directly to workspaces and runtime templates, with AES-encrypted just-in-time credential delivery.
On the application side, a new centralized Credential Store manages API keys, tokens, and passwords as first-class objects, supporting both inline encrypted storage and live references to cloud secret managers. Role-Based Access Control has been unified and restructured across the entire platform, with clearer role definitions and a new separation between module access and create permissions. Granular sharing now supports simultaneous public and group-level access at different permission tiers, and User-Defined Processes (UDPs) can now be shared with "Use Only" access, allowing teams to consume custom logic without viewing or modifying the underlying implementation.
## AI-Powered Assistance
Syntasa 9.1 introduces two new AI capabilities. The Syntasa AI Help Assistant is a multi-agent system powered by LLMs and a LangGraph orchestration layer that provides contextual guidance based on where a user is in the platform, surfacing relevant documentation, troubleshooting steps, and configuration guidance in real time.
The AI-Powered Log Analysis agent scans job execution logs, identifies root causes, and provides specific remediation suggestions. Unknown errors are saved to a Known Error Database, improving future incident response across the organization.
Additionally, every process in the development palette now displays an in-app tooltip description covering over 100 standard processes, with support for UDP authors to add their own descriptions as well.
## Job Execution Improvements
GPU acceleration is now available for both interactive notebooks and batch Spark jobs across AWS EMR, GCP Dataproc, and Azure, powered by the NVIDIA RAPIDS Accelerator for Apache Spark. Production-grade defaults are included out of the box.
The execution log experience has been redesigned end-to-end: stack traces are automatically collapsed, a new tabbed JupyterLab modal consolidates kernel diagnostics, and concurrent multi-user log access is fully supported.
A new Code Managed process mode gives advanced developers complete control over data reads, writes, partition management, and state, bypassing platform automation for workflows that require custom merge logic, Delta Lake operations, or non-table outputs.
## Superset Analytics Upgraded to 6.0.0
The integrated Superset analytics engine has been upgraded from 5.x to 6.0.0, bringing a React 18 frontend rebuild, Ant Design v5, native dark mode, dataset folders, and security groups. All Syntasa-specific plugins have been migrated and optimized. The backend now runs on Python 3.11.
## Management Console Enhancements
Platform administrators gain new operational controls in 9.1, including selective pod-group redeployments, in-console deployment scaling and pod management without requiring kubectl access, automated SSL/TLS certificate conversion and cluster-wide distribution, Magic Link password resets, and significantly faster dashboard load times.
For the complete list of changes, visit the [Syntasa 9.1.0 Release Notes](https://help.syntasa.com/hc/en-us/articles/35546280594845-Syntasa-9-1-0). To plan your upgrade, contact your Syntasa account team.
---
# Syntasa Data & Agentic AI Platform Listed in AWS US Intelligence Community Marketplace (ICMP)
URL: https://syntasa.com/insights/syntasa-aws-intelligence-community-marketplace
Date: 2026-06-05
Category: NEWS
Audience: Mission
New listing makes it easy for IC agencies to discover and deploy the Syntasa Data & Agentic AI Platform in secure, classified cloud environments
Mclean, VA – June 5, 2026 – Syntasa, the Data and Agentic AI company, today announced that the Syntasa Data & Agentic AI Platform is now listed in AWS US Intelligence Community Marketplace (ICMP). AWS ICMP is a curated digital catalog designed exclusively for intelligence agencies to evaluate, procure, and deploy software in secure cloud environments. The listing makes it easy for IC agencies to discover and deploy Syntasa in support of their data and agentic AI initiatives.
> “Making the Syntasa platform available in the AWS ICMP is an important step in our mission to put advanced data and AI capabilities into the hands of organizations that need them most,” said Jay Marwaha, Founder and CEO at Syntasa. “This listing enables IC agencies to leverage our integrated data, AI, and agentic platform in secure, classified cloud infrastructures — accelerating their journey from raw data to intelligence to action.”
The Syntasa Data & Agentic AI Platform provides capabilities to create integrated pipelines for ingesting and unifying data from disparate sources, applying AI and machine learning, generating intelligence and insights, and taking action through agents and activations. The platform is deployable in secure, classified cloud infrastructures and is designed to run natively within a private cloud environment, keeping sensitive data inside the perimeter.
## About Syntasa
Syntasa is the Data and Agentic AI company. The Syntasa platform delivers an integrated data engineering + data science + agentic workflow that enables organizations to move from raw data to actions — in weeks, not months. Syntasa is available on the AWS ICMP and is deployable in secure classified cloud environments. To learn more, visit syntasa.com.
If you are interested in this product, or ICMP in general, please contact AWS_ICMP@jdiss.cia.ic.gov or icmp@amazon.com for more information.
## Explore More
- [Syntasa Replaces Databricks for Faster, Smarter Data Activation](/insights/syntasa-replaces-databricks-for-faster-smarter-data-activation)
- [Syntasa Achieves Awardability on the DoW's Tradewinds Solutions Marketplace](/insights/syntasa-dow-tradewinds-marketplace)
- [Syntasa Data + AI Platform Joins Google Cloud Ready — Distributed Cloud](/insights/syntasa-google-distributed-cloud)
- [Data and AI Platform](/platform)
---
# One Product, Every Channel: How a Real-Time Product Intelligence Hub Powers Your AI Agents
URL: https://syntasa.com/insights/one-product-every-channel-ai-agents
Date: 2026-05-27
Category: POV
Audience: Enterprise
You invested in AI agents. You saw the demos, you approved the budget, and you went live. But the customer experience isn't what you expected.
You invested in AI agents. You saw the demos, you approved the budget, and you went live. But the customer experience isn't what you expected. Shoppers are still getting generic answers. The agent doesn't know who they are. It doesn't know what they're looking at, what they intend to buy, or whether it's even in stock. The conversation falls flat, the sale doesn't happen, and the revenue spike you were promised hasn't arrived.
This isn't a problem with the AI. It's a problem with what you're feeding it.
The models are good. The infrastructure for deploying agents has matured fast. But without the right product context underneath, your agents will keep underperforming no matter how much you spend on them. Here's what that looks like in practice:
- The agent can't tell a customer whether the product they're looking at is available in their size
- It recommends items that are out of stock or discontinued
- It can't read the intent of the visitor, so it shows the wrong pricing at the wrong moment
- No email nudge gets triggered because the system doesn't know the customer was ever close to buying
- When a shopper asks a specific question, the agent either hedges or gets it wrong
Every one of those failures costs revenue. And none of them are fixed by a better LLM. They're fixed by fixing the product data layer.
## The Data Your Agents Actually Need
Think about what a shopping agent needs to answer a question well. Not just customer data. Not just a static product catalogue from your PIM. It needs to know right now: Is this item in stock? In which sizes? What are customers saying about it? What are people who browse this product actually buying instead? Is there a promotion running on it today?
That information lives in at least four or five different systems for most retailers. Inventory sits in your OMS or ERP. Reviews live in a third-party platform. Behavioural signals are in your analytics stack. Promotional data is in your commerce platform. Each of those systems updates on a different schedule, managed by a different team, and none of them were designed to talk to an agent in real time.
So when your agent gets asked "is this in stock in a size 10?" it either can't answer or it answers with data that's 24 hours old. That's not an AI failure. That's an infrastructure failure.
## Why MDMs and CDPs Don't Solve This
A lot of platforms are rushing to claim "context intelligence" right now, and it's worth being specific about what they actually offer versus what they don't.
| Capability | MDM | Traditional CDP |
|---|---|---|
| Product data unification | Yes (descriptive only) | No |
| Real-time inventory | No | No |
| Behavioural signals | No | Partial |
| Customer intelligence | No | Yes |
| Agent activation | No | Limited |
| Single source for all channels | No | No |
Master Data Management tools unify product data at a descriptive level. They're excellent for governance and consistency across your catalogue. But they don't carry real-time inventory. They don't ingest behavioural signals. They don't activate against agents. They're a data store, not an intelligence layer.
Customer Data Platforms give you customer profiles, behavioural history, and first-party data activation. They understand who your customer is. But they have no native concept of product intelligence at the depth agentic commerce requires. They know the customer. They don't know the product in real time.
This distinction matters a lot as your agent strategy matures. A customer-aware agent that doesn't know your product catalogue is only half-informed. It can personalise the experience, but it can't close the loop on the actual transaction.
## One Source. Every Channel. No Duplication.
Most brands have to set up product data separately for every channel that needs it. Their website agent gets one feed. Their Unified Commerce Platform gets another. Someone builds a separate integration for ChatGPT, another for Gemini. Every team is pulling from different sources, maintaining different pipelines, and the data is never quite in sync.
Syntasa's product intelligence hub works from a different premise. You centralise product data once, and every channel reads from the same live source simultaneously.
Here's what that looks like in practice. Your product data, including real-time inventory, review signals, pricing, and behavioural data, flows into one hub. That hub simultaneously powers three things.
First, your own shopping agent on your website. The agent knows exactly what's in stock, what's performing well, and what your customers are signalling interest in at that moment. It doesn't need to query three different systems. It reads from one.
Second, your Unified Commerce Platform, which handles the commerce layer: whether that's a native checkout experience directly within an AI interface or a shopping agent sitting on your brand's own site. Product data has to be accurate at the point of purchase, regardless of where that purchase happens.
Third, the external LLMs: ChatGPT, Gemini, and others. When a customer starts their journey in an AI interface and asks about your products, those models need accurate, structured product data to surface the right answers. The Universal Commerce Protocol is how that data gets to them. And because it all originates from the same centralised hub, you're not running separate pipelines for each destination, you're not managing separate integrations per team, and you're not reconciling differences between what your website agent knows and what Gemini knows.
Set up once. Power everything. No separate integrations. No stale data. No duplicated effort.
## Product Data is Now a Discovery Layer
This is the shift most digital teams haven't fully internalised yet. Product data used to be about making sure your Product Detail Page (PDP) had accurate information. Now it plays a direct role in AI-powered discovery.
When someone searches in AI Mode on Google, or asks ChatGPT to help them find running shoes for a half marathon, the AI is making product recommendations based on the structured data it can access. If your product data isn't there, is incomplete, or is stale, your products don't get surfaced. The conversation happens without you.
And when a customer does find your product through an AI interface and wants to complete the purchase, that same data has to support the transaction: accurate sizing information, real stock levels, current pricing. The discovery moment and the commerce moment are collapsing into the same interaction. Product intelligence has to be ready for both.
## The Three Layers That Make an Agent Actually Useful
Product intelligence doesn't operate in isolation. Syntasa's approach brings together three layers that need to work in concert for an agent to be genuinely useful rather than just present.
The first is brand context. This is the organisational logic: your rules, your intent, your journey design. What should an agent do when a customer asks about a return? What products should it prioritise? What tone reflects your brand? Agents without this layer behave like they have no institutional knowledge, because they don't.
The second is product intelligence. This is where most brands have the biggest gap. Real-time inventory, reviews, behavioural signals, and product relationships, all from one hub, accessible to every agent and every channel without duplication.
The third is customer intelligence. First-party data, behavioural history, and predictive signals that tell the agent who this customer is and what they're likely to need next.
Most platforms can give you one or two of these. The combination of all three, with product intelligence as the often-missing piece, is what separates an agent that actually moves revenue from one that becomes an expensive demo.
## Why Product Intelligence is the Differentiator for Retail
The stakes are particularly high for retail. Large catalogues mean more surface area for data to go stale. Fast-moving inventory means a response that was accurate an hour ago might be wrong now. Complex product relationships, including sizing, compatibility, variants, and bundles, mean an agent needs more than a flat catalogue to answer questions well.
This is not a problem you can solve with better prompting. You can't engineer your way out of bad product data in the context layer. The only fix is getting product intelligence right at the source and making sure it flows to every agent and channel from one place, in real time.
## The Practical Question
If you're a VP of E-commerce or Head of Digital thinking about your agentic commerce roadmap, the question worth asking isn't "which agent should we deploy?" It's "what data are we giving it, and how fresh is it?"
Because the agents your competitors are deploying are largely running on the same models you can access. The differentiation isn't going to come from the LLM. It's going to come from the intelligence layer underneath it.
Brands that get their product data centralised, live, and connected to every channel from a single source will build agents that actually know what they're talking about. The rest will keep wondering why their AI investment isn't delivering.
Syntasa's product intelligence hub centralises real-time inventory, reviews, and behavioural signals in one place, simultaneously powering your shopping agent, your Unified Commerce Platform, and external LLMs including ChatGPT and Gemini. One source. No duplication. No stale data.
---
# Syntasa Achieves ISO/IEC 27001 Certification, Reinforcing Commitment to Enterprise-Grade Data Security
URL: https://syntasa.com/insights/syntasa-achieves-iso-27001-certification
Date: 2026-05-15
Category: NEWS
Audience: Mission, Enterprise
Syntasa today announced that it has achieved ISO/IEC 27001 certification, the internationally recognized standard for information security management systems (ISMS). The certification validates Syntasa's systematic approach to managing sensitive data, mitigating risk, and maintaining the confidentiality, integrity, and availability of information across its platform.
Data security is no longer a secondary concern. As organizations operationalize artificial intelligence (AI) and machine learning (ML) across enterprise workflows, the volume and sensitivity of data in motion have increased significantly. At the same time, regulatory scrutiny and customer expectations continue to rise. Many enterprises now require ISO 27001 certification as a baseline for vendor selection.
The challenge is straightforward: you need to move faster with data while reducing risk exposure. That tension is difficult to resolve without formalized controls and independently validated processes.
ISO/IEC 27001 provides that framework. By achieving certification, Syntasa demonstrates that it has implemented and maintains a comprehensive ISMS aligned with global best practices. This includes:
- Continuous risk assessment and treatment processes
- Formalized security policies and governance structures
- Access control and identity management protocols
- Secure development and deployment practices
- Ongoing monitoring, auditing, and improvement mechanisms
These controls are not theoretical. They are audited by an independent third party and must be continuously maintained to retain certification. For Syntasa customers, the outcome is clear:
- Reduced vendor risk during procurement and compliance reviews
- Greater confidence in how data is handled, processed, and protected
- Faster time-to-value when deploying data-driven use cases
- Alignment with internal security and governance requirements
> "Syntasa is built to help organizations operationalize data and AI at scale. Achieving ISO 27001 certification reflects the discipline behind that mission. It ensures that as our customers accelerate innovation, they do so on a foundation that meets the highest standards for security and trust."
>
> — Jay Marwaha, Founder and CEO at Syntasa
This milestone builds on Syntasa's broader commitment to enterprise readiness, including scalable infrastructure, privacy-first design principles, and measurable business outcomes driven by agents and AI.
Security is not a feature. It is a requirement. ISO 27001 certification ensures that requirement is met — consistently, transparently, and at scale.
## About Syntasa
Syntasa enables organizations to transform data into measurable mission and business outcomes through Agents and AI. By operationalizing data, agents, and AI across the enterprise, Syntasa improves efficiency, increases revenue, and enables smarter decisions and actions.
---
# Syntasa Achieves Awardability on the DoW’s Tradewinds Solutions Marketplace
URL: https://syntasa.com/insights/syntasa-dow-tradewinds-marketplace
Date: 2026-05-14
Category: NEWS
Audience: Mission
McLean, VA – February 20 – Syntasa, a security-first data and AI software company, today announced that its Data + AI Platform has been designated “Awardable” through the Chief Digital and Artificial Intelligence Office’s (CDAO) Tradewinds Solutions Marketplace.
The Tradewinds Solutions Marketplace is the flagship acquisition vehicle within the Department of War’s (DoW) Tradewinds initiative, designed to accelerate the procurement and deployment of Artificial Intelligence (AI), data, and analytics capabilities across mission environments.
With its Awardable status, Syntasa enables DoW customers to rapidly acquire enterprise-grade Data + AI capabilities purpose-built for secure and classified operations.
> "Achieving Awardable status in the Tradewinds Solutions Marketplace represents an important milestone in our commitment to advancing DoW modernization priorities. Defense agencies can now accelerate mission-critical initiatives with secure, scalable AI and data capabilities deployed directly within their controlled environments."
>
> — Jay Marwaha, CEO at Syntasa
## Deploy Secure, Mission-Critical AI
Syntasa’s Data + AI Platform is a security-hardened, cloud-native COTS solution delivering full functional parity across IL5, IL6, and Top Secret environments. It provides a unified, single-pane-of-glass experience that eliminates cloud complexity, supporting both notebook-based development and robust low/no-code workflows. With proven portability across AWS, Azure, GCP, and on-premise deployments, the platform supports multi-cloud strategies, while AI-assisted migration tools and deep enterprise replacement expertise reduce transition risk.
Accelerated Time-to-Value: Pre-integrated, mission-ready platform with ATO across multiple IL5/TS environments enables transitions in months instead of years with minimal operational disruption. Access ready-made use cases for image processing, geospatial analytics, sentiment analytics, anomaly detection, identity resolution, and unified profiles.
Predictable Cost Governance: Unified control plane, capped enterprise licensing, and intelligent autoscaling deliver budget certainty and 15–25% reductions in compute spend.
Enhanced Analyst Productivity: Centralized AI workspace with native AI lifecycle tools, governed Feature Store, AI Code Assistant, and low/no-code workflows streamline pipelines and expand user access.
## Mission Impact Demonstrated
Syntasa was recognized among a competitive field of applicants to the Tradewinds Solutions Marketplace whose solutions demonstrated innovation, scalability, and potential impact on DoW missions.
## Next Steps
A demo video of Syntasa’s technology is accessible only by government customers on the Tradewinds Solutions Marketplace. You can view the listing here.
You can also learn about how Syntasa is replacing Databricks in C2E for customers in the intelligence community [here](/insights/syntasa-replaces-databricks-for-faster-smarter-data-activation).
Government customers interested in viewing the video solution can create a Tradewinds Solutions Marketplace account at tradewindAI.com.
About Syntasa: Syntasa is a security-first data and AI company focused on giving organizations complete control of their data, infrastructure, and outcomes. Trusted by defense and intelligence agencies, government organizations, and global enterprises, Syntasa builds platforms that operate inside the customer’s own cloud to support secure, large-scale analytics and mission-critical decision-making. Its Data + Agentic AI Platform enables governed data engineering, advanced analytics, and machine learning in highly regulated environments, including IL5, IL6, and Top Secret.
For more information or media requests, contact: info@syntasa.com
About the Tradewinds Solutions Marketplace: The Tradewinds Solutions Marketplace is a digital repository of post-competition, readily awardable pitch videos that address the DoW’s most significant challenges in the Artificial Intelligence/Machine Learning (AI/ML), data, and analytics.
All awardable solutions have been assessed through complex scoring rubrics and competitive procedures and are available to Government customers with a Marketplace account. Government customers can create an account at www.tradewindai.com. Tradewinds is housed in the DoW’s Chief Digital Artificial Intelligence Office.
---
# Neo Unplugged From the Matrix. Your Campaigns Should Too.
URL: https://syntasa.com/insights/neo-unplugged-matrix-your-campaigns
Date: 2026-04-30
Category: POV
Audience: Enterprise
Enterprise marketing teams are running blind across channels. Here is what waking up looks like.
## You Think Your Campaigns Are Running. Look Closer.
There is a scene early in The Matrix where Neo is sitting at his desk, doing his job, believing the system works. Everything looks functional. Everything looks connected. It is not.
Most enterprise marketing teams are living that scene right now.
The dashboard shows campaigns running. The AI is generating outputs. The reports are being sent on time. But underneath, the web is in one tool, email is in another, paid media is in a third, and three different teams are working from three different definitions of what success looks like. The campaign is live. The moment it was built for has already passed.
This is not a data problem. It is an execution problem. And the first step out of it is acknowledging that more tools will not close a gap that more tools created.
## The Matrix Your Stack Built
The marketing technology landscape now includes over 15,000 tools, yet marketers use only 33% of their stack's capabilities, down from 58% in 2020. (Chiefmartec / Gartner, 2026) More tools has not meant more execution. It has meant more fragmentation.
71% of marketers say they struggle to keep up with how buyers move across platforms. (HubSpot State of Marketing, 2026) The channel explosion created a tool explosion, and the tool explosion created a coordination problem that does not show up on any single dashboard because no single dashboard can see all of it. That invisibility is precisely what makes it dangerous.
## The Cost Nobody Is Tracking
A campaign that should take two days to launch takes two weeks. Segment logic gets rebuilt from scratch in a separate tool. Copy gets briefed to a separate team. Performance can only be reviewed after someone manually pulls reports from three platforms. By the time everything is connected, the customer it was designed for has moved on.
45% of project managers spend more than one full working day per week simply compiling status updates across campaigns. (Wrike / Forbes, 2026) That is time that used to belong to strategy. It has been quietly reassigned to coordination.
The deeper consequence is organizational. Brand, demand generation, and paid teams end up targeting the same customers with no shared view of what is working. Attribution becomes a political exercise, with every team claiming credit and no one able to prove it. (Cometly, 2026) Enterprise teams that unify their campaign execution report 40% better collaboration and nearly doubled delivery speed. (Advaiya / Digital DI Consultants, 2026) Which tells you exactly what fragmented teams are leaving on the table.
## Why AI Is Still the Blue Pill
The industry's answer has been AI. 81% of marketing technology leaders are either piloting or have already implemented AI agents. (Gartner, 2025) Yet the execution gap has not closed, and the reason goes deeper than most vendors will admit.
Most marketing AI is only as good as the data it can see. And in a fragmented stack, it cannot see very much. When your web campaign data lives in one tool, your email performance in another, and your paid media in a third, the AI you have invested in is making decisions on partial information. It is not slow or unintelligent. It is just working blind.
Beyond the data problem, most marketing AI is still assistive rather than operational. It advises. It suggests. It generates. And then it hands the work back to the marketer to execute manually across the same disconnected tools as before. Worse, most AI tools place the burden of usefulness on the user. Prompt engineering has become an unofficial job requirement, a new skill layer added on top of an already complex workflow. As one fractional CMO put it in the 2026 Future of Marketing Report: "AI will tell us what is trending, but not why it matters. Dashboards will flood us with numbers, but not with narrative." (CMO Alliance, 2026)
The problem has never been access to AI. It is that most AI was deployed into a fragmented data environment where it can only ever see part of the picture — and partial data produces partial answers, no matter how sophisticated the model sitting on top of it.
## What the Red Pill Looks Like
Syntasa's Campaign Studio, part of the 9.1 release, does not add to the stack. It replaces the coordination layer that the stack never had. Web, Email, and Paid Media campaign management come into a single workspace where every campaign is visible by status, channel, and performance in one place.
The workflow is three steps: Define, Personalize and Deliver, and Activate. Campaign logic can be saved as reusable rules, which means the targeting built for a Q4 retention push does not get rebuilt from zero in Q1. The Campaign Health Dashboard gives teams a live view across all three channels simultaneously, making it possible, for the first time, to make channel decisions with full context rather than partial metrics.
The AI Agents embedded in Campaign Studio work differently from most marketing AI. They are built on Context Engineering: business context is configured once at the platform level, and every agent conversation that follows works from that foundation. Teams define their campaign goal, their customer, their intended outcome. The agents work from there in natural conversation, no prompt engineering required.
The Customer Insights Agent turns a plain-English question into a data query without a ticket to the analytics team. The Customer Audience Agent converts a natural-language description into precise segment logic inside the existing data environment. The Email Personalization Agent generates copy grounded in actual campaign goals and customer context, not generic templates. The model flips: instead of marketers learning to talk to AI, the platform learns to talk to them.
> "The gap between having customer data and being able to act on it has never been a data problem. It has been an execution problem."
>
> — Jay Marwaha, CEO, Syntasa
## Take the Red Pill
Campaign velocity is no longer a function of team size or budget. It is a function of architecture. The teams that will pull ahead are not the ones with the most tools. They are the ones who finally decided to look closer at what their stack was actually doing, and made the architectural change to fix it.
The teams that pull ahead in the next two years will not be the ones that added the most tools. They will be the ones that made a deliberate architectural decision to unify how campaigns are built, executed, and measured. That decision is not a technical one. It is a strategic one, and it belongs on the CMO's agenda, not the marketing ops backlog.
If your campaigns are running across three tools, your performance data is living in silos, and your AI is generating outputs that someone still has to manually act on, the gap is not going to close on its own. The question worth asking is not which tool to add next. It is what it would look like to finally remove the coordination layer entirely.
That is the conversation worth having.
---
# Exploring AI-Assisted Migration from Databricks to Syntasa in C2E
URL: https://syntasa.com/insights/ai-assisted-migration-databricks-to-syntasa-c2e
Date: 2026-04-23
Category: POV
Audience: Mission
Discover how Syntasa's AI Migration Assistant cuts Databricks migration timelines from months to weeks while preserving business logic and reducing risk.
For many organizations, the modern data stack has become harder to manage than the problems it was meant to solve. What began as a flexible, scalable environment for data processing has gradually turned into a patchwork of notebooks and dependencies. Costs are difficult to predict. Governance is fragmented. AI initiatives stall between experimentation and production. And as complexity grows, even simple changes begin to carry disproportionate risk.
The issue is not capability. These platforms are powerful. The issue is what happens over time. As workflows expand and teams scale, environments become tightly coupled to proprietary features and operational habits that are difficult to unwind. Thus, the very thing that made teams feel agile and fast just a short while ago, has begun to make them feel stiff and slow.
This is the point at which many organizations begin to reassess.
Artificial intelligence has changed how organizations think about data platforms. The conversation is no longer limited to storage, processing, or scalability. It is about how quickly data can be turned into action, and how reliably that action can be delivered across the enterprise.
That shift reframes the role of the platform itself. It is no longer just infrastructure. It is the engine that connects data, models, and decisions. And when that engine begins to strain under complexity, migration becomes a strategic consideration rather than a technical one.
## Why traditional migrations struggle
Most data environments are not designed with migration in mind. They evolve over time, accumulating layers of logic, dependencies, and custom workflows that reflect years of operational decisions.
For instance, a typical Databricks environment will include tens of thousands of notebooks. Many of these rely on platform-specific features, tightly coupled APIs, and optimizations that do not translate easily to other systems. Over time, business logic becomes embedded within these constructs, making it difficult to separate what must be preserved from what can be replaced.
In a traditional migration model, this complexity has to be unraveled manually. Code is reviewed, dependencies are mapped, and entire workflows are at times rewritten from scratch. Even with experienced teams, this process is slow and tedious.
The result is a migration timeline that can stretch to 12–18 months. During that period, organizations must maintain parallel systems, manage duplicated costs, and accept the possibility of inconsistencies between old and new environments.
For teams operating in secure or mission-critical settings, that level of uncertainty is difficult to justify.
## A fundamentally different approach
Syntasa approaches migration from a different starting point. Rather than treating it as a manual engineering challenge, it treats it as a problem that can be systematically automated and controlled.
At the center of this approach is a set of proprietary AI-assisted tools, combined with a structured migration methodology that has been tested in highly demanding environments. The goal is not simply to move code from one platform to another, but to do so in a way that preserves functionality, minimizes disruption, and accelerates time to value.
This is not a theoretical proposition. The approach has been applied in environments where failure is not an option, including Impact Level 5 and 6 (IL5 and IL6), and top secret deployments. In these contexts, migration must be both precise and predictable.
By combining automation with proven processes, Syntasa reduces migration timelines from years to months while maintaining full operational continuity.
## The role of the AI Migration Assistant
The most significant shift in this model comes from the use of the Syntasa AI Migration Assistant.
In traditional migrations, the most time-consuming work involves identifying platform-specific dependencies and rewriting them in a way that preserves business logic. This is also where most errors occur. Subtle differences in behavior can lead to inconsistencies in output, which are often only discovered late in the process.
The AI Migration Assistant addresses this directly.
It analyzes existing codebases and automatically identifies proprietary constructs that need to be transformed. These may include utilities, optimization layers, or catalog dependencies that are unique to the source platform. Once identified, these elements are refactored into open-source equivalents that are compatible with the Syntasa environment.
Importantly, this transformation does not alter the underlying business logic. The intent is to preserve behavior while changing the implementation.
What would previously have required months of manual effort can now be completed in weeks. The impact is not just speed, but consistency. Automation removes much of the variability that makes traditional migrations unpredictable.
## A phased approach that prioritizes continuity
Automation alone is not enough to guarantee a successful migration. It must be combined with a process that controls risk at every stage.
Syntasa applies a four-phase methodology that is designed to maintain continuity while steadily progressing toward full transition.
The process begins with discovery and assessment. Rather than relying on manual documentation, automated scanning tools are used to inventory the entire environment. This includes workloads, dependencies, and any proprietary features that will require transformation. The output is a detailed migration plan with clear timelines and resource requirements, eliminating uncertainty before work begins.
Once this foundation is in place, Syntasa is deployed within the organization's existing cloud environment. Crucially, this happens without disrupting current operations. Data remains within its existing security boundary, and existing systems continue to function as normal. The new platform is established alongside the old, creating a stable base for migration.
The third phase is where the AI Migration Assistant operates at scale. Code is automatically refactored and adapted to the new environment, with human oversight ensuring that performance and correctness are maintained. This combination of automation and validation allows the process to move quickly without sacrificing quality.
Finally, validation and cutover are handled with a level of rigor that is often missing from traditional approaches. Workloads are run in parallel across both platforms, and outputs are compared automatically. Only once results are confirmed to be consistent does the final transition take place. At every stage, rollback options are preserved, ensuring that any issue can be addressed without disruption.
## Reducing risk without slowing progress
One of the most important aspects of this approach is how it reframes risk.
Instead of concentrating risk into a single cutover event, migration is broken into controlled stages. Lower-risk workloads are migrated first, allowing patterns to be established and confidence to build. More complex pipelines follow once the process has been proven.
Parallel operation ensures that there is always a fallback, while automated validation provides continuous assurance that outputs remain consistent. This combination allows organizations to move quickly without taking unnecessary risks.
In practice, this means that migration is no longer something that must be carefully avoided or indefinitely postponed. It becomes a manageable, controlled process.
## Maintaining familiarity while enabling change
A common concern during migration is the impact on teams. New platforms often require retraining, changes to workflows, and a period of reduced productivity.
Syntasa addresses this by maintaining compatibility with familiar tools and frameworks. Existing Spark-based workflows continue to function; Delta tables and common file formats are fully supported; and notebook environments are preserved through JupyterLab. Code can be integrated with existing Continuous Integration/Continuous Deployment (CI/CD) pipelines without modification.
This continuity means that teams can continue working as they always have, even as the underlying platform changes. Migration does not require a reset. It allows organizations to evolve their capabilities without losing what already works.
## Beyond migration: operational advantages
While the migration process itself is important, the long-term value lies in what the new platform enables.
Syntasa brings together capabilities that are often fragmented across multiple systems. Data engineering, analytics, and machine learning are unified within a single pro/low/no-code environment, reducing the need for context switching and simplifying collaboration.
AI is integrated directly into the platform rather than treated as a separate layer. Feature stores, experiment tracking, model management, and monitoring are all part of the same workflow. This allows organizations to move more quickly from experimentation to production, and to maintain greater control over model performance.
At the same time, the platform introduces stronger governance over cost and resource usage. Intelligent autoscaling, automated cluster management, and unified visibility help organizations reduce unnecessary spend, with typical compute cost reductions in the range of 15–25%.
The architecture itself is built on open-source frameworks, ensuring that organizations retain flexibility over time. This reduces the risk of vendor lock-in and makes future transitions easier, should they be required.
## Migration in a C2E context
In a [Cloud-to-Edge (C2E)](/insights/from-cloud-to-edge-public-sector-c2e) context, the role of the data platform becomes even more critical.
Data is no longer confined to centralized systems. It must move quickly between cloud environments and the points where decisions are made. This requires platforms that can support not only large-scale processing, but also real-time activation and operational delivery.
Migration to Syntasa supports this shift by bridging the gap between data infrastructure and decisioning. It enables organizations to move beyond batch processing and toward a model where data and AI are continuously applied across the enterprise.
This is where the value of migration becomes most apparent. It is not just about replacing one platform with another. It is about enabling a different way of working with data.
## From disruption to opportunity
Migration has traditionally been viewed as a disruptive necessity. Something that must be managed carefully and, if possible, delayed.
AI-assisted approaches challenge that view.
By combining automation, structured methodology, and real-world experience, Syntasa turns migration into a process that is faster, safer, and more predictable. More importantly, it turns it into an opportunity.
Organizations are no longer limited to preserving what they already have. They can use migration as a moment to completely modernize their approach to data, integrate AI more effectively, and build a platform that supports operational outcomes rather than just analysis.
In that sense, migration is no longer the end of one system. It is the beginning of a more capable one.
To learn more about how Syntasa makes it easy to migrate your system, [speak to one of our consultants](/#brief).
---
# From Data to Decisions: How Syntasa Operationalizes AI in the Enterprise
URL: https://syntasa.com/insights/from-data-to-decisions-operationalizing-ai
Date: 2026-03-30
Category: POV
Audience: Enterprise
From churn prediction to real-time recommendations, see how Syntasa helps enterprises deploy AI inside their own cloud.
Artificial intelligence has become a defining priority for enterprise organizations. Across industries, leadership teams are investing heavily in predictive models, automation, and advanced analytics.
Yet despite this momentum, most organizations still struggle to translate AI into measurable business outcomes.
The issue is not a lack of data. Enterprises today capture vast volumes of customer, product, and behavioral data across websites, mobile apps, commerce platforms, and advertising systems.
The issue is execution.
Data is fragmented across systems. Analysts spend significant time preparing datasets rather than analyzing them. Models are developed in isolated environments and can take months to deploy. Even when insights are generated, activating them across real marketing and commerce workflows remains slow and inconsistent.
Many platforms compound this challenge. Data must be moved into vendor environments, reshaped to fit proprietary schemas, or reduced to meet pricing constraints. As a result, organizations spend more time managing infrastructure than generating value.
At the same time, pricing models tied to data volume or user counts create artificial limits. Teams are often forced to train models on a fraction of available data to control costs, reducing accuracy and limiting impact.
The result is a persistent gap between AI ambition and business performance.
Syntasa closes that gap.
## A Different Approach to Enterprise AI
Syntasa is an open-architecture AI platform that enables organizations to run advanced intelligence directly within their own cloud environment.
Rather than operating as a [traditional SaaS](/insights/saas-cdp-vs-warehouse-native-cdp) platform, Syntasa brings compute to the data. It runs inside existing infrastructure such as AWS, Google Cloud, or Azure, ensuring that data never leaves the organization's governance perimeter.
This zero-copy approach eliminates the need for data movement, reduces latency, and simplifies compliance with privacy and regulatory requirements.
It also changes how organizations deploy AI.
Instead of forcing teams to adopt a monolithic platform, Syntasa provides a [composable](/insights/composable-before-it-had-a-name) AI layer. Organizations can select and deploy only the models and agents they need, aligning capabilities directly to business priorities.
This approach allows teams to address specific use cases – such as churn prediction, audience creation, or product recommendations – without committing to a full platform rollout. It also avoids paying for unused functionality, a common issue with bundled solutions.
Because Syntasa runs in the client's cloud environment, organizations can fully utilize their data without introducing cost-based constraints. Teams can focus on model performance and business outcomes rather than managing platform limits.
## The Limitations of Traditional CDPs
Customer data platforms (CDPs) were designed to unify customer data and make it accessible to marketing teams.
In practice, however, many CDPs introduce new layers of complexity.
Most require organizations to copy data into proprietary environments. This creates duplicate storage costs, increases latency, and complicates governance.
Many also enforce rigid data schemas, requiring extensive extract, transform, and load (ETL) processes before data can be used for modeling. Data engineering teams often spend months preparing data rather than building value on top of it.
Pricing models based on data volume or monthly tracked users introduce further constraints. To control costs, teams frequently down-sample their data, training models on only a portion of available history.
This directly impacts model accuracy and limits the effectiveness of AI-driven decisioning.
Finally, many platforms operate as "black boxes." They generate predictive outputs, but do not provide visibility into how those outputs are created. This lack of transparency makes it difficult for data teams to validate, trust, or improve models.
Syntasa addresses each of these challenges directly.
## Composable AI: Flexibility Without Compromise
At the core of the Syntasa platform is a [composable AI architecture](/platform).
This allows organizations to deploy specific AI capabilities as needed, rather than adopting a fixed suite of tools.
Teams can select from a range of pre-built models and AI agents, including churn detection, cart abandonment prediction, audience segmentation, product recommendations, and price sensitivity modeling.
These capabilities can be deployed independently and integrated into existing workflows, enabling rapid time to value.
For example, a global electronics retailer used Syntasa to unify fragmented customer interactions into cohesive profiles and deploy recommendation models across digital channels. The result was reduced cart abandonment and improved conversion rates across the customer journey.
Because Syntasa operates directly within the organization's cloud environment, these models can be trained on 100% of available data.
This removes the trade-off between cost and accuracy that is common in volume-based pricing models. Organizations can leverage their full data history to generate more precise predictions and more effective targeting strategies.
This modular approach is particularly valuable in large organizations, where different teams have distinct priorities. Marketing, data science, and commerce teams can deploy and iterate on use cases independently while still operating on a shared data foundation.
## Open Intelligence: From Black Box to Glass Box
One of the most significant barriers to AI adoption is trust.
In many organizations, data science teams are asked to rely on proprietary models without visibility into how they work.
Syntasa takes a different approach.
The platform provides a "glass box" environment, giving data teams full access to model logic and code.
This enables organizations to audit predictions, tune model parameters, and adapt models to their specific business context. It also ensures that organizations retain ownership of their intellectual property.
At the same time, Syntasa supports both technical and non-technical users.
Data scientists can build and deploy custom models using familiar frameworks such as Python, TensorFlow, or PyTorch.
Marketers and business users can interact with AI through no-code agents, enabling them to generate insights, build audiences, and activate campaigns without relying on engineering resources.
This hybrid model improves collaboration between teams and increases confidence in AI-driven decisions.
## AI Agents: Operationalizing Intelligence
Syntasa extends its capabilities through a set of AI agents designed to automate analysis and decision-making.
These agents monitor data in real time, identify patterns, and trigger actions based on predefined rules or model outputs.
Examples include detecting early signals of customer churn, identifying high-intent visitors during live sessions, generating audience segments, and delivering personalized content across channels.
Because agents can be deployed independently, organizations can address specific use cases without introducing unnecessary complexity.
In one deployment, Syntasa analyzed billions of data points in real time to detect anomalies and alert teams before issues impacted marketing performance.
This type of automation reduces operational overhead while enabling faster, more responsive decision-making.
## From Customer Intelligence to Product Intelligence
Syntasa brings together both customer intelligence and product or commerce intelligence within a single platform.
This allows organizations to understand not only who their customers are, but also how they interact with products, categories, and inventory.
Capabilities include category affinity detection, review summarization, and real-time product recommendations based on behavioral and contextual signals.
For example, organizations can tailor product experiences dynamically based on browsing behavior, predicted intent, and inventory availability.
This integrated view enables more effective personalization and more efficient merchandising strategies.
## Zero-Copy Architecture: Control, Security, and Speed
Syntasa's [zero-copy](/insights/first-party-mode-composable-cdp) architecture is a critical differentiator.
By running directly within the organization's cloud environment, the platform ensures that data never leaves its governance perimeter.
This provides stronger data security, simplifies compliance, and reduces the risks associated with third-party data handling.
It also improves performance. By eliminating the need for data movement and external processing, organizations can operate on live data and deliver real-time decisioning at scale.
For IT and data leadership, this approach simplifies architecture while maintaining full control. For business teams, it enables faster access to insights and more responsive execution.
## Real-World Impact at Scale
Syntasa has been deployed by [global enterprises](/customers) operating at significant scale.
Organizations have used the platform to process billions of behavioral events, unify data across dozens of systems, and create tens of millions of customer profiles.
In one example, a retailer implemented Syntasa to power AI-driven product recommendations, generating over £23 million in incremental revenue through improved targeting and bundling strategies.
In other deployments, organizations have achieved improved audience match rates, more efficient media spend, faster campaign execution cycles, and increased conversion rates across digital channels.
These results are achieved without introducing additional operational complexity. Because data, models, and activation workflows are unified within a single environment, organizations can scale AI initiatives without scaling overhead.
## Bridging the Gap Between AI and Execution
The challenge facing most organizations is not collecting data.
It is turning that data into intelligence, and turning that intelligence into action.
Syntasa addresses this challenge by combining a composable AI architecture, transparent modeling capabilities, zero-copy data processing, and real-time activation.
Together, these capabilities enable organizations to operationalize AI at scale, within their existing infrastructure, and without compromising control or flexibility.
## Ready to Operationalize AI in Your Data Environment?
Syntasa enables organizations to deploy AI where it matters most – directly within their own data environment and aligned to real business priorities.
If you are looking to move beyond experimentation and turn data into measurable outcomes, [Syntasa](/#brief) can help.
---
# Syntasa Adds Conversational AI Agents and a Campaign Studio for Enterprise Marketers as Part of Its 9.1 Release
URL: https://syntasa.com/insights/conversational-ai-agents-campaign-studio-enterprise-marketers
Date: 2026-03-30
Category: NEWS
Audience: Enterprise
McLean, VA — Syntasa today took a further leap into adopting Agentic AI in its platform as it released its 9.1 version. This is a significant step forward in how enterprise marketing teams manage campaigns, activate customer intelligence, and work with AI.
The release introduces two foundational capabilities that redefine how marketers operate: a Campaign Studio that consolidates Web, Email, and Paid Media campaign management into a single interface, and three Conversational AI Agents guided by Business Context enabling teams to interact with AI through natural conversation, grounded in high-level business context rather than engineered prompts.
## The Problem This Solves
Enterprise marketing teams today face a two-sided execution problem.
On one side: campaign management is fragmented. Web campaigns live in one tool. Email in another. Paid media in a third. There is no unified view of campaign health, no single place to act, and no shared context across channels. Teams spend more time managing their tools than managing their strategy.
On the other side: AI in marketing still demands too much from the user. Most tools require precise prompt engineering to produce useful outputs putting the burden on marketers to learn a new discipline just to access intelligence that should already be available to them.
The 9.1 release is built to solve both.
## Campaign Studio
At the core of the new release is a campaign management interface that brings every campaign across Web, Email, and Paid Media into a single, actionable workspace. Marketers can now view all campaigns by status, channel, and performance in one place. The three-step campaign workflow: Define, Personalize & Deliver, Activate, standardizes execution across channel types without sacrificing channel-specific configuration. Campaign logic can be saved as reusable rules, applied across multiple campaigns, and managed from a single dashboard without switching tools or losing cross-channel context.
The Campaign Health Dashboard gives teams a live view of performance across all three channels simultaneously making it possible, for the first time, to make channel decisions with full-picture context rather than siloed metrics.
## Context Engineering: A New Way to Work with AI
The 9.1 release introduces three Conversational Marketing AI Agents, each built on Context Engineering — a layered design principle that shifts the responsibility of AI interaction from the user to the platform.
At its foundation, Context Engineering operates through a structured framework of context layers. Technical system instructions and industry-level business instructions are configured behind the scenes, ensuring the agents understand how to operate, communicate, and apply domain-specific logic without exposing complexity to end users. Client-level technical rules further tailor the agent to each organization's data schema and standards.
On top of this foundation sits the Business Context layer — high-level, intent-driven settings that business users configure once to define the current mission: who the customer is, what the campaign goal is, what segment or outcome they're focused on. This is the "set it and forget it" layer that shapes every conversation that follows.
With that context in place, teams don't need to craft precise prompts to get useful outputs. Instead, the agents engage in natural, back-and-forth conversation, taking business instructions in real time — clarifying questions, refining parameters, and acting on intent rather than literal commands. The outcome is AI that works the way a knowledgeable colleague would: already briefed on the mission, ready to take direction and deliver results.
### Customer Insights Agent
Transforms plain-English business questions into precise data queries, delivering visualized intelligence directly within the platform. Teams no longer need to raise a request to the data team to understand what is happening in their customer base. They ask. The agent answers.
### Customer Audience Agent
Converts high-level audience descriptions into precise, actionable segment definitions. A marketer can describe a target in their own language — "high-value customers showing early signs of churn in the last 60 days" — and the agent builds the segment logic within their existing data environment. No rule-builder expertise required.
### Email Personalization Agent
Generates personalized subject lines and email body copy at scale, informed by customer context, campaign goals, and the business context set by the team. This is not template substitution. It is personalization grounded in the full picture of who the customer is and what the campaign is trying to achieve.
## Executive Perspective
> "The gap between having customer data and being able to act on it has never been a data problem. It has been an execution problem. The 9.1 release closes that gap by giving marketing teams one place to run their campaigns and marketing AI that meets them where they are, not where the technology requires them to be."
>
> — Jay Marwaha, CEO, Syntasa
> "Context Engineering changes the relationship between marketers and AI tools. When you stop asking people to engineer prompts and start asking them to describe their business intent, you get faster results and broader adoption. That is what we built for."
>
> — Charmee Patel, VP - Data & AI, Syntasa
## Snapshot of What Comes with the 9.1 Release
The 9.1 release includes:
- **Home Dashboard:** A unified workspace featuring campaign health performance cards, an activity feed, and AI-recommended next steps surfaced through the Smart Workspace.
- **Campaign Studio:** Unified management of Web, Email, and Paid Media campaigns with a standardized 3-step workflow, campaign rules library, and cross-channel performance dashboards.
- **Customer Insight Agent:** Conversational natural language to data query capability, grounded in business context.
- **Customer Audience Agent:** Plain-English to segment definition capability, operating within the existing data environment.
- **Email Personalization Agent:** Context-informed personalized copy generation at scale.
- **Profile Attributes:** Centralized visibility into the global attribute layer powering customer profiles.
- **Identity Resolution:** Golden Record matching (deterministic and probabilistic) operating within the enterprise's existing cloud infrastructure.
## Availability
The 9.1 release is now available for existing Syntasa customers and qualified enterprise prospects.
## About Syntasa
Syntasa is a warehouse-native, modular platform powered by agentic AI — unifying customer data to deliver smarter personalization, deeper insights, and real-time activation at scale.
## Explore More
- [Customer Insights Agent: Query Your Data in Plain English →](https://syntasa.com/resources/customer-insights-agent-ask-questions-get-answers/)
- [Customer Audiences Agent: Build Audiences on the Go →](https://syntasa.com/resources/customer-audience-agent-build-in-minutes/)
- [Agentic Marketing Platform: Super-Agent Led Marketing →](/enterprise)
- [Explore All Customer Success Stories →](/customers)
---
# Syntasa Unveils Industry-First Agentic Marketing Platform to Orchestrate Enterprise Intelligence
URL: https://syntasa.com/insights/syntasa-unveils-agentic-marketing-platform
Date: 2026-02-25
Category: NEWS
Audience: Enterprise
Syntasa, a pioneer in warehouse-native data solutions, today announced the launch of its Agentic Marketing Platform. This category-defining solution moves beyond isolated AI assistants to provide a cohesive ecosystem where AI doesn't just "chat," but plans and executes complex workflows 24/7 according to an enterprise's specific business ethos and business processes.
## A "Super Agent" Guided by Business Ethos
Unlike traditional AI tools that operate as "black boxes," Syntasa's platform is designed to internalize an organization's specific business ethos and business processes. Through a natural language interface, users can set a business context at a high level, and the Super Agent autonomously decides which specialized agents to invoke to achieve the goal—all within a single chat interaction.
## Modular Independence and Open Architecture: Pay for What You Use
True to Syntasa's roots as a Composable CDP, the platform is designed for maximum flexibility and scalability. Individual agents—such as the Customer Insights or Email Personalization agents—are engineered to operate independently. This modularity and openness allows enterprises to deploy specific autonomous capabilities directly into their existing stack without requiring the entire end-to-end platform, ensuring that organizations only pay for the specific intelligence they use.
## The Specialized Agent Ecosystem
The Agentic Marketing Platform features a diverse and evolving library of agents categorized to handle every facet of the marketing lifecycle:
- **Knowledge & Onboarding Agents:** Deeply research "What data and code do I have?" to facilitate seamless organizational intelligence.
- **Analytics Agents:** Transform plain English questions into complex SQL queries against raw warehouse data, delivering visualized insights in seconds rather than days.
- **Creative Agents:** Generate hyper-personalized email copy, ad creative, and website banners at a scale humans cannot reach.
- **Decisioning Agents:** Autonomously manage audiences, campaigns, and customer journeys, ensuring strategic logic is executed without manual intervention.
- **Agents for Customers:** Deployed directly on digital surfaces to support service discovery, product discovery, and conversational commerce.
Here's a sneak peek into one of our agents:
## Zero-Copy, Model-Agnostic Execution
To ensure maximum flexibility and security, the platform is entirely model-agnostic, supporting industry-leading AI models such as Gemini, Claude, and OpenAI. By bringing the agent to the data (Zero-Copy), Syntasa ensures that all planning and execution occur securely within the enterprise's existing cloud environment (GCP, AWS, or Azure), maintaining strict data governance while eliminating the need for "stripped down" data transfers.
## Democratizing Data Access
The Agentic Marketing Platform is designed to feel like conversing with a colleague, empowering non-technical team members to bypass technical syntax and IT backlogs. By automating the "heavy lifting" of data segmentation and insight generation, teams can move at the speed of their curiosity and engage customers at the exact moment of intent.
As part of the rollout, Syntasa is releasing new specialized agents regularly and is currently offering a structured pilot program for organizations to test the platform against their real-world data and business processes.
## About Syntasa
Syntasa provides a warehouse-native, modular, open architecture data and agentic AI platform, that captures and unifies customer data to enable personalization, segmentation, and AI. Through its Composable CDP architecture, Syntasa provides full transparency and scalable data intelligence, helping enterprises transition from passive insights to autonomous execution.
## Explore More
- Build Behavioural Audiences in Minutes with AI-Powered Intelligence
- Query Customer Data with The Customer Insights Agent
- [View our Google Cloud Marketplace listing](https://console.cloud.google.com/marketplace/product/syntasa-public/syntasa-composable-cdp)
- [Explore all Customer Success Stories](/customers)
---
# From Data to Action: How Agentic AI Unlocks Real-Time Customer Intelligence
URL: https://syntasa.com/insights/from-data-to-action-agentic-ai
Date: 2026-02-23
Category: POV
Audience: Enterprise
Syntasa's Agentic AI runs inside your cloud to deliver governed, transparent AI for real-time audiences, insights, and personalization—without moving data.
Enterprise teams are not short of data. What they struggle with is turning that data into timely, governed, and commercially meaningful action.
Marketing teams wait days for audience builds. Growth leaders rely on analysts to answer operational questions. AI platforms promise automation yet frequently operate as opaque systems that cannot be audited or tuned. Meanwhile, gaining access to advanced capabilities often requires sensitive first-party data to be moved outside the organization's governance perimeter.
This friction is cumulative. By the time a segment is built, validated, and activated, the opportunity window may have shifted. By the time a performance question is answered, the campaign has moved on. What enterprises lack is not information; it is immediacy, coherence, and control.
Syntasa's approach to Agentic AI addresses this gap by activating intelligence directly within the enterprise cloud environment, eliminating the need to trade sovereignty for speed.
## Intelligence Without Compromise
At the architectural level, the principle is straightforward: AI should run where the data lives.
Rather than exporting customer data into a vendor-controlled SaaS environment, Syntasa operates directly inside the enterprise's private cloud infrastructure – whether AWS, GCP, or Azure. This zero-copy model brings compute to the data, removing the need for duplication or egress and reducing the exposure surface for sensitive information.
This approach fundamentally alters the conversation around sovereignty. Enterprises no longer need to choose between advanced AI and governance integrity; they can retain full control over first-party data while applying intelligence at scale.
Transparency is equally important. Many AI solutions provide predictive outputs without exposing the underlying logic, creating a trust gap between automation and adoption. When data science teams cannot inspect or adjust model parameters, they are understandably reluctant to operationalize those outputs across critical business workflows.
Syntasa takes a glass-box approach, delivering production-ready models while preserving full visibility into the code and logic beneath them. Data scientists can audit, tune, and extend models to fit their context, maintaining ownership of both methodology and IP.
The economic model reinforces this flexibility. Because Syntasa runs in the client's cloud, it does not impose a volume-based licensing tax. Enterprises can train and score against 100% of their historical data without worrying about escalating costs. That matters for predictive accuracy; models perform better when they are trained on complete behavioral histories rather than artificially reduced datasets.
The result is a [composable AI layer](/platform). Instead of purchasing a monolithic suite to access a single capability, enterprises deploy only the agents and models required to solve immediate business challenges.
## Customer Audience Agent
*Moving from manual segmentation to real-time activation*
Audience creation has long been a structural bottleneck in enterprise marketing operations. In many organizations, building a behavioral segment requires marketing to define intent; data teams to translate that intent into SQL; governance stakeholders to validate schema usage; and multiple rounds of iteration before activation.
This process routinely takes three to five days and requires significant manual effort. Beyond the operational cost, the commercial consequence is measurable: campaigns launched late underperform those activated on time. Naturally, when execution lags, conversion suffers.
The Customer Audience Agent compresses this cycle dramatically.
Instead of relying on ticket queues and manual query construction, marketers can describe audience goals in natural language. The agent interprets that intent, maps it to approved fields within the enterprise schema, validates the logic against live data, and produces an executable audience definition ready for activation.
Crucially, this workflow occurs entirely within the governed cloud environment. The agent operates under the same access controls and schema constraints that apply to human users, ensuring that automation does not introduce governance risk.
For marketing teams, the impact is speed. Complex behavioral segments that once required days can be built in minutes. For data teams, the impact is focus. Rather than servicing repetitive segmentation requests, they can concentrate on advanced modelling, experimentation, and strategic initiatives.
This is not simply a productivity gain. By eliminating segmentation latency, enterprises increase the likelihood that campaigns are launched within optimal windows, directly influencing revenue outcomes. Because Syntasa is composable, the Audience Agent can be deployed independently, allowing organizations to address this bottleneck without committing to a wholesale platform replacement.
## Customer Insights Agent
*From dashboard dependency to interactive intelligence*
If segmentation delays hinder activation, insight delays hinder decision-making.
Enterprise teams frequently depend on dashboards or analyst mediation to answer performance questions. A growth leader may want to understand which high-value users are exhibiting early disengagement signals or which product categories are trending within a specific cohort. In traditional workflows, these questions enter a queue, are translated into SQL, and are resolved days later.
By then, the business context may have evolved.
The Customer Insights Agent replaces this reactive cycle with direct, governed access to live data. Users pose questions in plain English, and the agent converts those prompts into structured SQL queries executed within the enterprise cloud environment. Results return in seconds, enabling teams to validate ideas and adjust tactics in near real time.
Because the agent runs inside the existing infrastructure, no data leaves the governance perimeter. The intelligence layer operates within the same security and compliance framework as the rest of the data stack.
The practical implications are significant. Growth teams can test hypotheses mid-campaign. Merchandisers can interrogate product performance without waiting for reporting cycles. Marketing leaders can refine targeting logic based on current behavioral signals rather than retrospective summaries.
Importantly, this does not diminish the role of data specialists. Instead, it elevates it. Routine exploratory queries become self-service, freeing data scientists and analysts to focus on model development, feature engineering, and experimentation that drive strategic differentiation.
## Email Personalization Agent
*Converting intelligence into measurable engagement*
Audience definition and insight generation establish the foundation; personalized activation converts intelligence into revenue.
Syntasa's Email Personalization Agent applies the same agentic framework to content generation. It produces hyper-personalized messaging aligned to behavioral micro-segments, ensuring that communication reflects actual user context rather than broad demographic assumptions.
The measurable impact of AI-driven outreach is well established. In donor-focused deployments, AI-powered personalization has delivered 20% increases in open rates; 66% improvements in click-through performance; 25% larger average contributions; and campaign revenue growth exceeding 125%.
While the domain differs, the principle remains constant: when messaging aligns with behavioral intelligence, engagement increases.
As with the other agents, personalization operates entirely within the sovereign architecture. Audience logic, insight generation, and message creation occur inside the enterprise cloud environment, eliminating the need to export sensitive customer data to third-party systems.
This continuity across segmentation, insight, and activation creates an integrated intelligence loop rather than a sequence of disconnected tools.
## Why This Matters Now
Markets are less tolerant of delay than ever.
Without agentic capabilities, segmentation remains slow, insight remains mediated, and messaging remains broadly targeted. AI models are adopted cautiously because they cannot be inspected. Data movement outside secure environments introduces compliance and security concerns that slow innovation.
With Syntasa's Agentic AI, the dynamic changes. Audiences are built in minutes rather than days. Insights are delivered interactively. Personalized messaging can be deployed immediately. All of it occurs inside the enterprise's private cloud infrastructure, preserving transparency and governance.
This resolves several long-standing enterprise tensions. Automation no longer requires opacity. Accuracy no longer depends on down-sampling to manage licensing costs. Speed no longer demands data egress. Intelligence no longer undermines sovereignty.
Agentic AI becomes an operational accelerator grounded in architectural discipline.
## A Pragmatic Path Forward
Large-scale AI initiatives often falter because they demand transformation before demonstrating value. By contrast, Syntasa's composable approach encourages incremental adoption.
Organizations can begin with a focused pilot, deploying a single agent to address a defined bottleneck. Usability is validated with a controlled group of users, performance impact is measured, and feedback informs refinement. Once confidence is established, additional agents can be introduced without re-platforming or restructuring the data environment.
This phased approach reduces risk while accelerating measurable return.
## From Data to Decisive Action
Enterprises already possess the behavioral, transactional, and engagement data required to drive intelligent growth. What they need is a mechanism to convert that data into timely, governed action.
Agentic AI provides that mechanism when it operates inside the enterprise cloud, respects governance boundaries, and remains transparent to the teams accountable for outcomes.
In this model, intelligence is not outsourced. It is activated.
Syntasa enables organizations to deploy the capabilities they need, compute across their full datasets, retain ownership of models and IP, and eliminate friction between insight and execution.
Agentic AI, implemented this way, does not diminish control. It strengthens it.
---
# From Cloud To Edge: Why Public Sector C2E Programs Are Moving to Syntasa
URL: https://syntasa.com/insights/from-cloud-to-edge-public-sector-c2e
Date: 2026-02-23
Category: POV
Audience: Mission
Public sector C2E programs require real-time AI, edge execution, and strict governance. See why agencies are moving beyond cloud-first platforms to Syntasa for secure, air-gapped, IL5/6 and TS-ready deployments.
Cloud-to-edge (C2E) programs are now common across defense, intelligence, and the wider public sector. That's because most missions do not run in a central cloud region. They run in field locations, on limited or unreliable networks, in restricted environments, and inside day-to-day operational workflows where decisions must be made quickly.
Edge computing exists to deal with that reality. It allows data to be processed closer to where it is created, which reduces delays and limits the amount of data that needs to be sent back to a central cloud.
This is where many C2E programs struggle. They treat cloud-to-edge as a data storage problem. In practice, it is an execution problem. C2E succeeds or fails based on whether analytics and AI can run where decisions are made, not just where data is stored.
This creates tension with cloud-first analytics platforms. One such platform, Databricks, is widely used and well suited to centralized analytics and large-scale data engineering. But C2E programs need more than analytics. They need governed, real-time execution across cloud, edge, and sometimes air-gapped environments.
That is why many public sector teams move beyond Databricks as their C2E programs mature.
## C2E is an Execution Problem, Not a Storage Problem
In public sector environments, "the edge" is not a design preference. It is a constraint.
Teams deal with intermittent connectivity, strict data residency rules, and locations where sending data back to a central platform is slow, expensive, or not allowed. In some cases, environments are fully disconnected—including IL 5, IL 6, and Top Secret (TS) environments where strict security mandates apply.
C2E platforms are judged on whether they can still function under those conditions.
That means answering a number of practical questions. For instance:
- Can data be ingested and processed continuously?
- Can analytics and models run close to operators?
- Can governance and audit trails remain intact across environments?
- Can analysts and operators work from the same data without building custom systems?
If the platform cannot do this on its own, teams end up building workarounds. Insights are created in the cloud, exported, translated, and pushed into other systems at the edge. Naturally, this is far from ideal. It adds delay, complexity, and risk.
## Why Databricks Falls Short in Public Sector C2E Architectures
To be clear, this is not a critique of Databricks' analytics capabilities. It is a question of fit.
Databricks is designed as a unified analytics and AI platform for cloud environments. Its documented architecture separates a managed control plane from compute resources running in the customer's cloud account. This model works well for centralized data engineering, analytics, and machine learning workflows.
But C2E introduces constraints that Databricks was not designed around as first principles.
### Intermittent or Limited Connectivity
Databricks relies on connectivity between users, the control plane, and compute resources. In environments where network access is limited or unreliable, maintaining that connectivity becomes an operational challenge. Teams often need additional infrastructure and custom logic to keep systems running.
### Air-Gapped and Restricted Environments
Databricks is positioned as a cloud platform that manages analytics workloads in public cloud environments. In fully air-gapped or classified settings—including IL 5, IL 6, and TS environments, that assumption breaks down. Supporting these environments typically requires additional tools and custom deployment patterns outside the core platform.
### Operator-Facing Execution
Databricks is optimized for analysts, data engineers, and data scientists. C2E programs often require more than analyst workflows. They require systems that support operators, mission owners, and leaders working from the same governed data layer, with outputs designed for action rather than exploration.
As a result, Databricks often becomes one part of a larger, custom-built C2E stack. The analytics live in Databricks, while execution at the edge is handled elsewhere.
## What Public Sector C2E Use Cases Actually Require
Across agencies and missions, C2E requirements tend to look similar.
C2E Requirement
What it Means in Practice
Continuous Ingestion of Operational Data
C2E systems ingest data from sensors, devices, applications, and operational systems. These data streams must run continuously, even when network connectivity is limited or inconsistent.
Real-Time Analytics and Inference
Many mission decisions cannot wait for batch processing. Models and analytics need to run close to where data is generated so prioritization, detection, and response can happen quickly.
Unified Identity Across Environments
Public sector data is spread across devices, users, sessions, and systems. C2E platforms need to connect these signals into a consistent view without forcing all raw data into one central location.
Governance and Auditability
Governance is a baseline requirement. Teams must be able to trace where data came from, how it was processed, and how decisions were made across both cloud and edge environments.
Federated Analysis
Agencies often need insight across domains without pooling sensitive data in one place. This requires distributed execution with strong access controls and data ownership preserved.
## How Syntasa is Built For Cloud-To-Edge from Day One
Syntasa approaches C2E from a different starting point.
It is designed as a data + AI platform that deploys entirely within the customer's environment—including private cloud, hybrid, on-premise, and edge deployments—as a Kubernetes application running inside the customer's designated Virtual Private Cloud (VPC). It supports no-code, low-code, and pro-code workflows so mixed technical teams can work in the same system.
Importantly for public sector use cases, Syntasa has achieved Authorization to Operate (ATO) in multiple IL 5, IL 6, and Top Secret environments, and supports air-gapped and cross-domain solution (CDS) deployments. The platform operates with complete data sovereignty—all data, processing, and platform operations remain within the customer's security boundary. The result is a single governed platform where data, analytics, and AI can operate consistently from cloud to edge, without custom engineering to bridge the gap.
Rather than assuming data must be centralized and then pushed outward, Syntasa is designed to run analytics and AI where the mission runs.
The platform also combines analytics, machine learning, and operational workflows in one system. That reduces the need to stitch together separate tools for preparation, modeling, activation, and monitoring.
## C2E Scenarios Where Syntasa Replaces Databricks
As C2E programs mature, teams often replace Databricks not because it fails, but because it was built for a different job.
### Edge-First Analytics
In many missions, data is produced at distributed sites. Edge-first analytics means generating insight locally or near-locally, instead of defaulting to central processing. This reduces delays and lowers reliance on constant backhaul.
Syntasa's ability to deploy within air-gapped and on-premise environments (with proven ATO in the most demanding classification levels) supports this model directly.
### Operational AI at the Edge
C2E programs increasingly rely on models that run in near real-time. These models need to execute even when connectivity is limited. Platforms that assume constant cloud access introduce risk.
Running AI directly in the execution environment reduces that dependency. Syntasa's ML Runtime supports the full machine learning lifecycle, including model serving with automatic scaling, within the customer's security boundary.
### Cross-Domain Analytics with Controls
When data cannot be centralized, teams need ways to analyze across environments without breaking governance rules. Syntasa supports distributed execution while keeping data ownership and controls intact, with immutable audit logs and role-based and attribute-based access controls aligned to classification levels and mission roles.
### Human-in-the-Loop Workflows
C2E is not just about automation. Analysts, operators, and leaders often need to work together. Syntasa's unified collaborative workspace (supporting notebook, low-code, and no-code workflows) is designed to support shared workflows where data, analysis, and action live in the same governed layer.
## Governance, Security, and Control in C2E Environments
Public sector C2E programs demand more than standard cloud security.
They often require:
- Private or sovereign deployments
- Air-gapped execution
- Strict auditability
- Clear data ownership boundaries
Databricks' architecture includes managed control plane services that remain external to the customer environment. This is appropriate for many cloud analytics use cases. In sensitive C2E environments, however, it can introduce additional complexity.
Syntasa deploys as a Kubernetes application entirely within the customer's VPC, with no data traversing external networks or third-party infrastructure. The platform provides cryptographically signed immutable audit logs, AES-256 encryption at rest, TLS 1.3 in transit, and supports NSA Type 1 encryption devices in TS environments. Its architecture inherits the security controls and ATO of the hosting environment, allowing authorization processes to complete in 60–90 days rather than 6–12 months typical of external SaaS services. This allows teams to meet governance and security requirements without building custom platforms from scratch.
## Choosing a Platform for C2E is a Strategic Decision
Databricks remains effective for centralized analytics teams working primarily in the cloud. It is a strong choice when the goal is large-scale data engineering and analysis.
But it's important to recognize that C2E programs are different. They are judged on whether data and AI can move with the mission.
Forcing a cloud-first analytics platform into edge-first missions increases engineering overhead, slows delivery, and raises operational risk. In mission environments, that delay is not just technical—it directly affects response time, coordination, and outcomes.
Choosing a platform designed for distributed execution reduces that burden. Syntasa's capped enterprise licensing model also eliminates usage-based cost escalation, providing predictable annual budgeting—an important factor for agencies managing multi-year programs.
## Conclusion: C2E Programs Win when Data + AI Move with the Mission
C2E programs succeed when analytics and AI can run across cloud and edge environments, including restricted and disconnected locations.
Edge computing exists to bring execution closer to where decisions are made. Platforms that assume centralized execution struggle to meet that need without significant customization.
Syntasa is a data + AI platform built to operate across cloud, edge, and air-gapped environments, with proven ATO across IL 5, IL 6, and Top Secret deployments. For public sector teams running C2E programs, that alignment really matters.
Explore Syntasa's data + AI platform for C2E missions via its [data + AI](/platform) and [Public Sector](/mission) solution pages.
---
# How Emergent AI Expands What a Composable CDP Can Do Inside Your Cloud
URL: https://syntasa.com/insights/how-emergent-ai-expands-composable-cdp
Date: 2026-01-29
Category: POV
Audience: Enterprise
Learn how Syntasa's AI brings your CDP to life, making data smarter, decisions faster, and your teams more empowered.
## Why Emergent AI Changes the Composable CDP Equation
By now, most serious businesses realize that Composable CDPs are no longer a differentiator. They are the norm.
We're at a similar juncture to where cloud computing was a decade ago. What once was optional, is now assumed. The conversation has moved from whether to adopt them to what you can actually build on top.
Most serious data and marketing organizations have already moved away from monolithic, SaaS-bound customer data platforms toward warehouse-native or composable architectures. They did it for good reasons: data ownership, flexibility, governance, and the ability to evolve without replatforming. But competitive advantage is dynamic by nature, and systems that don't adapt eventually fail.
The truth is that AI innovation is now moving too fast for most Composable CDPs to operationalize. New modeling techniques, foundation models, agentic workflows, and real-time decisioning patterns are appearing continuously, yet many Composable CDPs still treat AI as a set of static features rather than a living operating layer.
The inevitable result is a growing gap between architectural flexibility and actual intelligence in production.
Emergent AI, which differs from traditional AI in that it is continuously learning and changing its behavior as conditions change, is the layer that determines whether a Composable CDP becomes a true decisioning system, or simply a better-organized data foundation.
## From AI Features to AI Operating Models Inside Composable CDPs
The relevant question is no longer "Does your CDP have AI?"
The real question is: "How does AI actually run inside the system?"
Many CDPs advertise AI capabilities, but those capabilities are often implemented as isolated features. For instance:
- A pre-trained churn score.
- A recommendation widget.
- A black-box propensity model.
These features may deliver short-term value, but they struggle to scale because they are not part of a coherent AI operating model.
As exciting as emergent AI is, it demands something fundamentally different to run properly.
- Direct access to raw and modeled first-party data.
- Explainability and auditability at every stage.
- Continuous iteration (not static models).
- Tight coupling between modeling and activation.
This is where SaaS-abstracted CDPs fall behind. When AI execution happens outside the customer's cloud – for instance, behind vendor-managed APIs or opaque pipelines – data scientists lose control, marketers lose trust, and iteration slows.
In other words, the effectiveness of deploying the most cutting-edge AI available depends heavily on where execution happens, not just which algorithms are available. Deploying advanced AI without the right execution environment is like dropping a Formula 1 engine into a family sedan and filling it with regular fuel. The engine may be world-class, but without the right platform to run it, you'll go nowhere fast.
## Three AI Modes that Matter in a Modern Composable CDP
A mature Composable CDP must support multiple AI modes simultaneously. Not as bolt-ons, but as first-class operating patterns.
Syntasa's approach is built around [three complementary AI modes](/platform), all running natively inside the customer's cloud environment.
### Pre-Built AI for Speed to Value
Pre-built AI (i.e., production-ready models and intelligence packaged for immediate use) exists for one reason: speed.
Production-ready models for propensity scoring, recommendations, or next-best-action allow teams to move quickly without waiting for bespoke data science cycles. When implemented correctly, they provide immediate lift and accelerate early adoption.
In the context of a Composable CDP, pre-built works best when:
- Models run on first-party data in the customer's environment.
- Inputs and outputs are transparent.
- Outputs are governed and auditable.
- Models can be overridden or extended when needed.
The limitation appears when pre-built AI becomes the ceiling instead of the floor. Static models, fixed features, and opaque logic eventually constrain teams that want to experiment, adapt, or differentiate.
Pre-built AI should accelerate value – not lock it in.
### DIY AI for Data Science Control
DIY AI (i.e., custom built ML developed and operated in-house using native data, tools, and workflows inside the customer's cloud environment) is where Composable CDPs either prove their worth or expose their limits.
For data scientists and analytics engineers, effective AI means:
- Native access to full-fidelity first-party data.
- Freedom to engineer features.
- Support for experimentation and retraining.
- The ability to deploy outputs directly into activation workflows.
When AI runs inside the same cloud environment as the data, teams can use their preferred frameworks, notebooks, and pipelines without friction.
Models move from experimentation to production without handoffs, exports, or reimplementation. This collapses the distance between analytics and activation. So does this benefit marketing teams too? Absolutely! They can consume AI outputs – scores, segments, recommendations – without waiting for reprocessing or vendor mediation, while data science retains ownership and control.
DIY AI turns the Composable CDP into a shared execution surface, not a handoff point.
### Agentic AI as the Next Layer of Automation
Agentic AI (i.e., autonomous or semi-autonomous systems that observe data, make decisions, trigger actions, and adapt their behavior over time based on outcomes and feedback) introduces a new operating pattern.
Instead of static models producing outputs on a schedule, agents observe data, evaluate conditions, trigger actions, and adapt workflows continuously. They can refine audiences, adjust thresholds, test variations, and respond to real-time signals without constant human intervention.
In a customer intelligence context, this enables use cases such as:
- Autonomous audience expansion and contraction.
- Real-time personalization adjustments.
- Journey optimization based on live behavior.
- Continuous experimentation loops.
However – and this is key – agentic AI only works when data, models, and activation live together. If any part of the loop is externalized, latency and governance break the system.
Importantly, agentic AI is additive. It does not replace analysts or marketers. It augments them by handling operational complexity while humans retain strategic oversight.
## Why Running AI Inside Your Cloud is Non-Negotiable
Emergent AI exposes architectural weaknesses quickly. When AI execution happens outside the customer's cloud:
- Latency increases.
- Data movement multiplies.
- Security boundaries blur.
- Explainability erodes.
In regulated or high-scale environments, this becomes unacceptable. Teams need to know:
- Which data was used.
- How models behaved.
- Why decisions were made.
- And how outcomes can be reproduced.
Running AI inside the customer's cloud environment changes the equation. It allows:
- Direct access to governed datasets.
- Full auditability of pipelines and outputs.
- Faster iteration through proximity to compute.
- Trust between technical and business stakeholders.
This is not an optimization. It is a prerequisite for sustainable AI-driven decisioning.
## Google + Syntasa as an AI Execution Environment
This is where Syntasa's [partnership with Google Cloud](/insights/syntasa-google-cloud-five-years-partnership) becomes decisive. It is not a branding exercise or an integration layer; it is an architectural alignment.
Syntasa runs natively inside Google Cloud environments, aligning directly with services such as BigQuery, Vertex AI, and native streaming infrastructure. This allows AI to operate where the data already lives – without replication, abstraction layers, or vendor-controlled black boxes.
For data scientists, this means: familiar tooling, elastic compute, native ML pipelines, and no forced migrations.
For marketers, it means: faster activation, governed AI outputs, real-time decisioning across channels.
The result is a Composable CDP that behaves like an AI execution layer – not just a data management system.
## What This Enables in Practice
In practice, this architecture enables patterns that are difficult or impossible to achieve with externalized AI.
Teams can deploy AI-driven audience expansion that is continuously refined by agentic workflows, without exporting data or rebuilding pipelines.
Predictive models can move from experimentation to activation in the same environment, eliminating replatforming friction.
Marketing teams can deploy AI-driven outcomes confidently, knowing that data science retains visibility, control, and governance.
These are not one-off optimizations. They are compounding capabilities.
## Emergent AI is the Differentiator Layer for Composable CDPs
Composable CDPs solved data ownership and flexibility. Emergent AI determines whether that flexibility becomes real intelligence in motion.
As AI innovation accelerates, the Composable CDPs that succeed will be those that treat AI as an actual operating model rather than a feature set. They will allow pre-built, custom, and agentic AI to coexist, iterate, and activate directly inside the customer's cloud.
Syntasa's approach combines composability with in-cloud AI execution, delivering transparency, control, and speed at scale.
That is what turns a Composable CDP into a true decisioning system.
## FAQs
### What is emergent AI in a composable CDP?
Emergent AI refers to AI patterns that evolve continuously – including custom models and agentic workflows – rather than static, pre-built features.
### Why does AI need to run inside the cloud?
Running AI inside the cloud ensures low latency, full governance, auditability, and direct access to first-party data.
### How is agentic AI different from traditional automation?
Agentic AI observes, decides, and adapts autonomously, rather than executing predefined rules on fixed schedules.
### Does this replace data scientists or marketers?
No. Emergent AI augments teams by automating operational complexity while preserving human control and strategy.
To learn more about how Syntasa can help you stay ahead of the competition with optimized AI deployment within your Composable CDP, [get in touch](/#brief).
---
# Wayne State University, Google and Syntasa: Transforming Public Health Assessments with AI
URL: https://syntasa.com/insights/wayne-state-university-public-health-assessments-ai
Date: 2026-01-08
Category: MENTIONS
Audience: Mission
Syntasa and Google Cloud have partnered with Wayne State University to deliver CHNA 2.0, combining PHOENIX population health data, cloud-scale analytics, and generative AI to modernize the federally required Community Health Needs Assessment into a faster, continuously updated process that gives public health leaders timely, actionable insight.
Originally published on the Google Cloud blog.
Source: https://cloud.google.com/blog/topics/public-sector/wayne-state-university-and-syntasa-transforming-public-health-assessments-with-ai/
---
# The Limitations of Reverse ETL Tools and The Next Evolution of the Composable CDP
URL: https://syntasa.com/insights/reverse-etl-vs-composable-cdp
Date: 2025-12-29
Category: POV
Audience: Enterprise
Reverse ETL moves data. A Composable CDP activates it. See why warehouse-native identity, AI, and governance outperform sync-only architectures.
The Composable CDP category is growing fast – but clearly not all architectures are created equal. A case in point is Reverse ETL ("extract, transform, load"), which many vendors use to move data out of their warehouse and into marketing platforms. Once upon a time, these tools solved a key challenge – getting data where it needed to go – but they are limited, and they stop short of activating intelligence.
Syntasa's Composable CDP represents the next evolution. Rather than sending data elsewhere, it runs natively inside your cloud to unify, model, and activate customer intelligence in real time. The result is a warehouse-native Composable CDP that gives enterprises full control over their data, governance, and performance.
## When Reverse ETL Hits Its Limits
Reverse ETL tools were designed to bridge the gap between a data warehouse and operational systems. They move tables or query results from storage into CRMs, advertising platforms and analytics environments.
But even when warehouse-native, this approach hits boundaries: identity often fragments, governance becomes more complex, and real-time decisioning can be constrained. Multiple synced copies across SaaS tools increase latency, duplicate data, and deepen compliance risk.
## How Syntasa Takes It Further
Syntasa also uses a warehouse-native model (deployed inside AWS, GCP or Azure inside the customer's environment) but it's built as a full customer intelligence platform rather than simply a sync engine.
It unifies ingestion (batch + real-time), identity resolution (persistent ID graph), and audience activation across all customer data.
Because activation, intelligence and audiences live inside the same environment, there is no need to push data into multiple downstream SaaS copies and manage disparate governance models.
The modular architecture (Data Ready → Core → Audience → AI) means teams can go from insight to activation without stitching standalone tools together.
For enterprises already invested in cloud infrastructure, it's a logical next step from "syncing data" to operationalizing data with identity, audiences and AI in one governed warehouse-native stack.
## Composable CDP = Reverse ETL + Identity + AI + Governance
In large enterprise stacks, Reverse ETL is only one node in a complex orchestration chain. Teams still need a warehouse for storage, notebooks or data build tool (dbt) for modeling; an identity graph for user stitching; and Reverse ETL for activation. Each new connection adds cost, latency, and risk.
Syntasa's Composable CDP collapses that entire chain. Running natively inside your existing GCP, AWS, or Azure environment, it unifies ingestion, identity resolution, AI modeling, and activation within one governed framework. For instance, instead of maintaining five vendors and a dozen syncs, teams operate a single composable layer where every function shares the same data, permissions, and context.
This architecture means faster pipelines, lower integration overhead, and complete control over compliance – all while keeping data inside the organization's [own cloud environment](/platform).
## Real-World Pain Points that Reverse ETL Won't Fix
Even the best Reverse ETL pipelines are not built to solve every enterprise need, even with strong sync capabilities.
### 1. No Built-In AI Modeling Environment
Most models are SQL or dbt-based; there is no full ML studio for training or deploying AI models inside your cloud. That means teams still need external tools to build predictions, recommendations, and next-best-action models.
### 2. Identity Resolution Is Still Model-Dependent
Most Reverse ETL tools can ingest behavioral event data into the warehouse and use it in audiences, but its identity resolution is still driven by the identifiers and rules you define across your warehouse models rather than a continuously managed CDP identity layer. Syntasa is warehouse-native, so identity stitching and behavioral context are maintained as part of the same in-cloud system that feeds analytics, AI, and downstream activation.
### 3. Activation Still Requires Syncing to the Last-Touch Tool
Hightouch activates by syncing warehouse data to destinations, including real-time audiences that can sync membership changes in seconds, and the final execution still happens in the destination tool. Syntasa works the same way at the last mile, but keeps decisioning, identity, and modeling warehouse-native so updates are computed where the data lives before syncing to Adobe Target, Meta, or email platforms.
## Action Data with Production-Ready AI and Attribution Inside Your Cloud
Reverse ETL stops the moment data lands in a destination. Syntasa's Composable CDP starts where activation-only tools end by giving teams access to a full library of prebuilt AI models, a DIY AI Studio, intelligent AI agents, and native attribution models that all run inside the customer's cloud environment.
- **Retail-grade recommendation engines:** Merchandising and eCommerce teams can deploy Syntasa's prebuilt or custom recommendation models that learn from browsing, purchase patterns, and predicted intent. A leading UK electronics retailer generated £23M in incremental revenue using Syntasa's AI-driven recommendations.
- **Churn detection tuned to your business:** AI models surface early behavioral signals of inactivity so marketers can trigger personalized retention journeys before a customer lapses – all using first-party data that stays inside your cloud.
- **Inventory-aware activation:** Prebuilt demand, stock, and price-sensitivity models connect product availability with real-time marketing decisions, helping teams prioritize offers that drive both conversion and margin.
Reverse ETL helps move data. Syntasa gives teams the AI to do something meaningful with their data immediately and securely using prebuilt models or models they can design themselves.
This combination gives marketers and data scientists something other tools can't: AI and attribution that train, deploy, and iterate directly on warehouse data, linking identity, behavior, outcomes, and media touchpoints with no copying, no black boxes, and no loss of granularity.
Pre-built models are tuned to your own data in one to two weeks, providing a fast path to production for use cases like propensity scoring, churn modeling, recommendations, and multi-touch attribution across paid, owned, and onsite channels.
Teams can also build full notebook-based models using Python, TensorFlow, PyTorch, and related frameworks directly on the unified dataset. Data scientists retain full control of their IP while attribution logic, features, and outcomes remain governed inside the customer's private cloud.
## Simplicity Without Sacrifice
Reverse ETL tools earned their popularity by being simple. But simplicity shouldn't require losing control. Syntasa's no-code and low-code interface gives marketers the same ease of use within a governed environment IT can trust.
Data teams keep full visibility into pipelines, lineage, and performance, while analysts and marketers enjoy intuitive workflows for audience creation, modeling, and activation. Because Syntasa runs on your own cloud, there's no vendor lock-in and no barriers to scale. It's as simple as Reverse ETL, but enterprise-grade from day one.
## Proof in Production
Two recent Syntasa deployments show how this architecture performs in practice.
### Case Study: AI-Driven Personalization and Real-Time Activation
A leading electronics retailer in the UK and Ireland implemented Syntasa's Composable CDP within its Google Cloud Platform environment. As a direct result, the retailer unified customer data across channels, creating over 22 million Customer 360 profiles.
In addition, real-time activation helped to generate £45 million+ in revenue from cart-abandonment campaigns and £20 million from web personalization initiatives.
By replacing fragmented batch processes with unified, real-time intelligence, they improved marketing efficiency, personalization accuracy, and revenue performance – all while maintaining full compliance with evolving privacy standards.
### Case Study: Automating and Scaling CDP Data Ingestion
A [global electronics retailer](/insights/syntasa-composable-cdp-global-electronics-retailer-case-study.pdf) deployed Syntasa's Composable CDP inside its private cloud to unify and operationalize customer data at enterprise scale. Prior to Syntasa, ingesting and managing data across web behavior, transactions, product data, consent signals, and contact center systems created fragmentation and slowed activation across regions. By standardizing ingestion pipelines and automating data readiness inside its own cloud environment, the retailer established a governed, real-time foundation for identity resolution, AI modeling, and activation. This approach enabled the creation of more than 169 million unified customer profiles and supported global personalization, paid media activation, and in-session decisioning without copying data into external SaaS systems. The result was a scalable, compliant data foundation that accelerated CDP maturity while supporting over $200M in incremental revenue across North America, EMEA, and APAC.
## The Next Step After Reverse ETL
Reverse ETL built the bridge between data warehouses and marketing tools. Syntasa takes the next step by unifying data, identity, AI, and activation directly inside the warehouse. It offers everything a Reverse ETL tool can, and then some. Syntasa's Composable CDP delivers:
- Real-time activation and millisecond decisioning.
- End-to-end governance under your existing cloud policies.
- Integrated AI modeling and predictive analytics.
- Flexibility to scale across teams, use cases, and industries.
For enterprises already running on GCP, AWS, or Azure, adopting a warehouse-native Composable CDP reduces complexity while improving insight speed and compliance confidence.
## Syntasa vs. Reverse ETL: Using Hightouch as a Use Case
Hightouch is a good example of a Reverse ETL tool that has slowly added on more capabilities to mimic a truly composable CDP. While it has its use-cases, these tools add layers of complexity and more limited functionality.
| Capability | Syntasa | Hightouch |
|---|---|---|
| Core Purpose | Unified Data + AI Platform for Composable CDP, modeling, and activation within your own cloud. | Data-activation layer that syncs warehouse data to external tools. |
| Architecture | Warehouse-native; runs fully inside GCP, AWS, or Azure with zero data movement. | SaaS-based; moves data out of the warehouse to 250+ connected apps. |
| Identity Resolution | Built-in ID Graph with deterministic and probabilistic matching for real-time stitching. | Identity resolution only available on certain product tiers. |
| AI & Predictive Modeling | Integrated propensity, churn, and GenAI modules for real-time insights and actions. | No built-in AI; relies on external ML pipelines or dbt integrations. |
| Real-Time Activation | Stream-based activation real-time decisioning across channels. | Only available on certain tiers, and relies on API calls subject to latency and sync schedule setting. |
| Ease of Use | No-code / low-code UI for marketers plus pro-code flexibility for data teams. | Simple UI for syncs; limited modeling and analytics tools. |
| Deployment Flexibility | SaaS, Virtual Private Cloud, On-Prem, or Air-Gapped for sensitive sectors. | Multi-tenant SaaS primarily; no private or on-prem option. Some hybrid deployments are documented but still rely on SaaS control plane. |
| Integration Scope | Hundreds of inbound/outbound integrations across martech, adtech, and data ecosystems. | 250+ outbound connectors for CRMs, ads, and productivity tools. |
| Security & Data Control | Segmentation happens within the customer cloud. Data never leaves the customer environment; fully auditable and transparent. | Data is read from warehouse during segmentation process; limited visibility post-sync. |
| Typical Users & Scale | Enterprise marketing, AI, and data teams managing billions of records securely. | Growth and RevOps teams syncing warehouse data to tools quickly. |
## Conclusion
Reverse ETL was an important step in the data-activation journey, but it's no longer enough for enterprises that demand speed, scale, and intelligence within their own environment. Syntasa's Composable CDP unites first-party data control, integrated AI, and warehouse-native design to deliver personalization and decisioning at enterprise scale, without data leaving your cloud.
[Explore how Syntasa helps organizations transform their warehouses into live engines of customer intelligence.](/#brief)
---
# SaaS CDP vs. Warehouse-Native CDP: How Architecture Shapes Enterprise Customer Intelligence
URL: https://syntasa.com/insights/saas-cdp-vs-warehouse-native-cdp
Date: 2025-12-19
Category: POV
Audience: Enterprise
Compare SaaS CDPs and warehouse-native CDPs. Learn how architecture affects data ownership, real-time decisioning, governance, and enterprise scale.
Customer Data Platforms are entering a new stage. Many organisations expect more than identity stitching or campaign lists. They expect real-time intelligence, direct control of their data, and infrastructure that aligns naturally with their existing cloud environments. This expectation is creating a clear divide between traditional packaged CDPs and warehouse-native Composable CDPs.
Early CDPs centralized customer data by extracting it from the warehouse into a vendor-managed schema. Composable CDPs run directly inside the customer's warehouse, executing capture, identity, analytics, AI, and activation without moving data.
Zeotap provides a packaged cloud CDP that centralises identity attributes, consent preferences, and customer profiles inside its managed environment, exporting that out to other tools via API connectors.
## Architecture: Two Paths with Distinct Implications
Packaged cloud CDPs typically centralise customer data and profile storage within a vendor-managed application layer. Zeotap follows this pattern by maintaining unified profiles inside its CDP environment.
Warehouse-native CDPs take a different architectural approach. Every Syntasa workflow from ingestion, identity, AI and activation, runs inside the customer's cloud, using existing data warehouses and object storage.
Traditional CDPs rely on ongoing data replication, creating structural vendor lock-in over time. Warehouse-native CDPs use zero-copy access, keeping data in place and avoiding proprietary storage dependencies.
This reduces the need for additional external data stores and allows governance and security controls to remain unified under the customer's policies.
## Identity, Intelligence, and Activation Working in Sequence
Zeotap offers identity and consent-based profile management. Its consent mappings, consent rules, and profile attributes operate as part of a unified profile store.
In a warehouse-native model, identity resolution and AI models execute directly against warehouse tables. This keeps identity graphs, analytics, and activation continuously aligned with live data.
Syntasa extends identity workflows with continuous intelligence through four modules that operate as a single system:
- **[Data Ready](/platform)** prepares and structures data for modelling and identity.
- **Core** builds a unified identity graph using deterministic and probabilistic methods.
- **[Audience](/platform)** enables real-time audience building and activation.
- **[AI](/platform)** runs predictive and generative models inside the customer's cloud.
Identity, analytics, and activation remain connected inside the customer environment, avoiding external data transfers.
## Real-Time Decisioning at Scale
Modern customer engagement relies on timely decisioning. Warehouse-native design supports streaming pipelines and millisecond-level orchestration. Syntasa can personalise on-site journeys, trigger session-based responses, and adapt content or offers during active browsing.
Packaged CDPs must first ingest events into their own storage, introducing latency and engineering overhead. Zeotap also supports real-time use cases, but the process is less intuitive, taking place through server-to-server ingestion and orchestration capabilities that must be manually built.
The key distinction lies in where the real-time decisioning executes: Because Syntasa executes real-time decisioning directly inside the customer's warehouse, it enables true in-session personalisation with millisecond latency; Zeotap must first ingest data into its CDP, introducing engineering overhead and response delay.
## Implementation Without Added Complexity
Syntasa avoids migrations and additional data layers. Deployment occurs inside the customer's cloud and integrates with existing pipelines, identity systems, and models. No duplicate storage is created. No new data capture layer is added. Teams can run Syntasa alongside an existing CDP before making platform-level decisions.
While Zeotap provides an accessible interface for marketing teams, it also introduces an external scaling dependency. The difference becomes visible when organisations manage multiple regions, large volumes of behavioural data, or specialised compliance conditions.
Where organisations require all data processing to stay inside their own cloud, warehouse-native deployment provides a streamlined path to integration with existing engineering and governance frameworks.
## Examples from Enterprise Practice
### Automating Data Preparation and Governance
Another large enterprise used Syntasa's Data Ready module to prepare data for Adobe Experience Platform. Schema creation, once a multi-day process, consistently completed in under 30 minutes. The improvement increased accuracy while reducing manual work for analysts and engineers.
## Comparison Overview
Syntasa operates as a zero-copy, warehouse-native CDP with no required data migration or pricing tiers, while Zeotap relies on vendor-managed storage and tiered SaaS pricing.
| Capability | Syntasa | Zeotap |
|---|---|---|
| Purpose | Warehouse-native Composable CDP operating inside customer cloud | Packaged cloud CDP with identity, consent, profiles, journeys, and activation |
| Architecture | Runs entirely within AWS, GCP, Azure customer accounts | Centralised profile and data management in Zeotap CDP |
| Identity | Deterministic + probabilistic identity graph | Identity attributes + consent metadata stored as unified profiles |
| Activation | Real-time streaming activation | Real-time S2S ingestion + real-time journeys orchestration |
| Governance | Inherits all customer cloud controls | Consent governance + enforcement inside Zeotap CDP |
| Integrations | 500+ inbound & outbound | Around 100 |
## Conclusion
As enterprises demand real-time intelligence and stricter governance, CDP architecture has become a strategic decision. Warehouse-native design aligns customer intelligence with long-term cloud and data strategy, restoring complete control.
SaaS CDPs and warehouse-native CDPs address different priorities. Many organisations now prioritise privacy, direct control, scalability, and continuous intelligence. Syntasa's warehouse-native architecture supports these needs by keeping all activity inside the enterprise cloud and providing a unified foundation for identity, modelling, and real-time activation.
This approach reduces complexity, strengthens governance, and provides a clear path for enterprise-scale customer intelligence.
## FAQs: Warehouse-Native vs. SaaS CDP
### 1. How does a warehouse-native CDP manage governance?
Governance inherits the organisation's cloud policies. No additional frameworks are required because the data remains inside the customer's environment.
### 2. Does a warehouse-native CDP require a data migration?
No. Data stays where it already resides. Pipelines, models, and activation operate in the existing cloud.
### 3. Can marketing teams use a warehouse-native CDP without technical expertise?
Yes. Syntasa provides a no-code and low-code interface for segmentation, activation, and analysis.
### 4. How does real-time activation function within the warehouse?
Event streams flow directly into the customer's cloud. Syntasa processes events immediately and triggers audiences, offers, or recommendations within milliseconds.
### 5. How long does implementation take?
Most organisations reach production in four to eight weeks because no data movement or external replication is required.
### 6. Can Syntasa integrate with existing ML models?
Yes. Models from Vertex AI, SageMaker, Databricks, or custom containers integrate without rebuilding.
---
# Why Google Cloud Multiplies Your Composable CDP Effectiveness
URL: https://syntasa.com/insights/google-cloud-multiplies-composable-cdp
Date: 2025-11-26
Category: POV
Audience: Enterprise
Run your CDP inside Google Cloud and turn your existing data stack into a real-time customer intelligence engine.
Many enterprises already rely on Google Cloud Platform (GCP) for analytics, AI, or data warehousing. Comparatively few, however, are tapping its full potential for real-time customer intelligence by running a composable Customer Data Platform (CDP) natively within the Google ecosystem. It's a little bit like installing solar panels on your roof only to sell that energy back to the grid and then continuing to draw power from the mains.
By deploying your CDP inside your own Google Cloud environment, every process – from data modeling to activation – runs within the same governed environment. You eliminate external friction, reduce latency, and gain total visibility and control over your data. It's the equivalent of directly using the solar energy produced by your roof panels to power the appliances in your kitchen. You already have the power; why not use it directly? If maximum efficiency is one of your goals, this is the way to achieve it.
## The Power of Staying Inside Your Cloud
When you deploy your CDP inside your GCP, you benefit from a zero-copy architecture: your data stays in your environment; you use your existing Identity & Access Management (IAM); Virtual Private Cloud (VPC); and governance policies; and you avoid moving data into third-party SaaS systems. As we've noted [before](/insights/composable-cdp-built-for-gcp): the difference between using a packaged CDP on GCP (which forces you to move your data into a vendor's system) and a [composable CDP](/insights/composable-before-it-had-a-name), is that a composable CDP lives directly [in your environment](/insights/first-party-mode-composable-cdp).
The implications are tangible: faster queries, no expensive egress fees, consistent governance controls, and fewer data silos. For example, one leading UK electronics retailer that implemented Syntasa's composable CDP in its GCP environment achieved a £23 million uplift in 2024 and £45 million+ revenue from cart-abandonment campaigns thanks to real-time behavioral triggers.
By staying inside your cloud, you also align your entire data-to-activation pipeline under your own security regime, thereby keeping compliance teams happy – never a bad idea.
## Activate Google Cloud's Native Muscles
Critically, it is important to understand that Google Cloud is far more than a simple storage platform. GCP is a full stack of services just waiting to be plugged into your composable CDP. Let's break down the main components and how they amplify CDP effectiveness:
### BigQuery – a unified data foundation
With BigQuery you store behavior, transaction, call-center, and other data in one serverless, petabyte-scale warehouse. It runs identity stitching, audience segmentation, and customer scoring directly in BigQuery, with no exports or data duplication.
### Vertex AI – produce smarter predictions
With Vertex AI (Google's managed machine learning platform), you can feed unified customer profiles into churn-prediction, next-best-action, or product-affinity models. This integration is crucial to enabling activation workflows inside the CDP stack.
### Pub/Sub – get real-time streaming
You can stream events from web, mobile, or CRM systems via Pub/Sub into your warehouse, enabling second-by-second updates. The result? Cart abandonment nudges or behavior-triggered offers can fire in-session (not hours later).
### Ads Data Hub – closed-loop activation
Activation and measurement live inside your cloud: segment audiences; push to Google Ads platforms; and measure response with minimal data movement. Native integration means faster insights and full data ownership.
And happily for those who fear being straightjacketed by vendor lock-in, each of these services is flexible: you turn them on when you need them. The outcome is a composable CDP that is not bolted on, but built into your cloud architecture.
## How It Works in Practice
Use Case
Google Cloud Component
Outcome
Cart Abandonment Recovery
BigQuery + Vertex AI
Real-time predictions fire offers mid-session
Cross-Channel Identity Graph
Pub/Sub + BigQuery
Device and account stitching occurs within seconds
Campaign Measurement
Ads Data Hub
Closed-loop attribution from your first-party data
AI-Driven Personalization
Vertex AI + Looker
Recommendations served instantly and at scale
## Governance, Cost, and Scale Advantages
- **Governance:** One IAM policy governs both data and activation workflows. When your CDP lives on Google Cloud, your existing identity and audit controls apply end-to-end.
- **Cost Efficiency:** Because there is no data movement out of your environment, you avoid egress fees and duplication costs. Businesses pay only for what they need, without vendor markup on storage or compute.
- **Scale:** BigQuery and Vertex AI scale seamlessly to petabyte-level workloads. The warehouse-native CDP approach enables enterprise-scale data volumes without typical SaaS caps or batch delays.
- **Compliance:** Google Cloud's certifications (SOC 2, GDPR, HIPAA) combine with your enterprise policies. A composable CDP inside GCP retains control and transparency of your data access and flows.
Running your CDP inside Google Cloud means efficiency isn't theoretical – it's built in. Because data never leaves your environment, every watt of compute goes toward insight, not transit. Think of the solar panels: you generate, consume, and optimize within one sustainable system.
## The Roadmap to Value
Syntasa platform makes it easier and faster to leverage these Google products and amplify your DCP. Here's a practical path for data engineers, solution architects, and marketing technologists to get the most value from this integration:
- **Step 1:** Connect your existing Google Cloud datasets into Syntasa modules: Data Ready → CDP Core → AI → Audiences.
- **Step 2:** Define segmentation or modeling workflows inside BigQuery using SQL or notebooks.
- **Step 3:** Build and deploy your Vertex AI models (e.g., propensity to buy, churn risk) and push predictions back into the activation layer.
- **Step 4:** Activate audiences and campaigns via Pub/Sub, Google Ads, and your CRM. Measure outcomes inside Ads Data Hub or Looker dashboards.
- **Step 5:** Iterate and scale: plug in advanced services only when ready, without rebuilding the stack.
All of this happens with one composable stack, no external data hops, and everything governed inside your Google Cloud environment.
## Conclusion
Google Cloud is not just infrastructure – and the sooner you can start thinking of it as a multiplier for your composable CDP, the better. When the platform, the data, and the activation logic live in the same ecosystem, speed meets security, and every prediction becomes actionable.
So if you're running on Google Cloud today and evaluating CDP strategies, the message is clear: Stay inside your cloud. Build a composable CDP. Activate your data in real time.
See how [Syntasa's Composable CDP](/insights/composable-cdp-complete-guide) runs natively inside your Google Cloud project and turns your CDP investment into a performance engine. [Schedule a technical walkthrough today.](/#brief)
## FAQs
### How does running a composable CDP inside my own Google Cloud project improve day-to-day decision-making for marketing and data teams?
Because the CDP runs directly in BigQuery, Vertex AI, Pub/Sub, and Ads Data Hub, teams work with a single, fully visible data environment instead of coordinating across a vendor's separate system. Segmentation, predictions, and activation all happen where the data already lives, so decisions can be made and acted on immediately rather than waiting on exports or syncs.
### What real performance gains should teams expect when activation, analytics, and modeling all live inside BigQuery and Vertex AI?
Teams see faster queries since there's no data movement between systems, real-time triggers like cart-abandonment nudges firing in-session rather than hours later, and AI workflows that move from Vertex AI model training straight into production activation without a separate deployment step.
### How does a zero-copy CDP architecture reduce costs without cutting back on use cases or data volume?
Because data never leaves the customer's Google Cloud environment, there are no egress fees or duplicate storage costs, and no vendor markup on top of BigQuery and Vertex AI compute. Businesses pay only for the cloud resources they use, without the volume-based pricing tiers or data caps that come with packaged SaaS CDPs.
### What changes for engineering teams when identity stitching, segmentation, and AI models run natively in GCP instead of external SaaS tools?
Engineering teams maintain far fewer pipelines, since there's no need to sync data out to a separate CDP and back. That also removes a common source of integration conflicts between systems, and keeps identity, segmentation, and modeling under one IAM and governance policy instead of reconciling access controls across multiple platforms.
### How does keeping CDP, AI, and activation inside Google Cloud strengthen compliance and security for enterprises operating at scale?
Keeping the CDP inside Google Cloud means it inherits the same IAM policies, VPC boundaries, and audit trails already governing the rest of the enterprise's data — plus Google Cloud's own SOC 2, GDPR, and HIPAA certifications. There's no separate vendor security posture to evaluate, since data access and flows never leave the customer's existing compliance perimeter.
---
# Two Smart Nudges that Turn Browsers into Buyers: AI and Composable CDPs for Cart Abandonment
URL: https://syntasa.com/insights/cart-abandonment-ai-composable-cdp
Date: 2025-10-30
Category: POV
Audience: Enterprise
Cart abandonment remains one of the most expensive problems in ecommerce. More than 70% of online shopping carts never make it through checkout, draining potential revenue from even the best-performing retailers.
Generic email reminders and blunt discounting strategies have been the default fix for years, but they rarely match customer intent. What's changed is the ability to act in real time, inside the customer's journey, with cart abandonment strategies that feel relevant.
This is where a [composable Customer Data Platform (CDP)](/insights/first-party-mode-composable-cdp) makes the difference. By unifying first-party data, enabling real-time orchestration, and layering in AI-driven decisioning, retailers can deliver nudges at exactly the right moment. The result is fewer abandoned carts, higher conversions, and happier customers.
## Why Composable CDP Transforms Cart Abandonment
The promise of highly personalized customer data via CDPs is nothing new. Since the beginning, legacy CDPs have promised (and delivered on) personalization. The issue is that at the same time they often lock data into proprietary systems, creating delays, limited activation, and increased compliance risk.
A cart abandonment CDP built on a composable architecture works differently:
- **Unified first-party data** – Browsing, purchase, and engagement data live in one warehouse.
- **Real-time activation** – Nudges are delivered in-session, not hours later.
- **[AI-driven orchestration](/insights/ai-module-customer-lifecycle)** – Predictive models decide which tactic (e.g., urgency, reassurance, or incentive) is most likely to convert.
- **Data ownership** – Retailers retain control in their own cloud environment.
This foundation takes the potential of high-level personalization and converts it into measurable results.
Below are two proven cart abandonment strategies, and how leveraging a composable CDP is the key to making them work.
## Cart Abandonment Strategy 1: Smarter, Personalized Nudges
Most cart abandonment strategies fail because they treat all shoppers the same. Composable CDPs, however, allow ultra-sensitive dynamic segmentation. For instance:
- High-value customers receive retention incentives.
- New shoppers see softer reminders, avoiding unnecessary discounts.
- Price-sensitive customers respond to urgency signals like "Only 2 left."
This level of subtlety and real-time marketing finesse is a game-changer. AI models predict which nudge will resonate, and the CDP activates it instantly.
For one leading UK electronics retailer that used this approach, the business managed to generate more than £45 million in incremental revenue through re-engagement of customers who had abandoned their carts. It was real-time CDP activation that turned abandoned baskets into completed sales.
## Cart Abandonment Strategy 2: Social Proof Ecommerce Tactics
Social proof ecommerce nudges reassure customers that they're making the right choice. Urgency signals remind them they may miss out if they wait.
With a composable CDP, these tactics are personalized in real time. For example, a message saying "200 shoppers bought this in the last 24 hours" might be shown only on high-demand items, while "4 people are viewing this product right now" may be displayed for hesitation-prone categories. For online customers prevaricating over whether to go to checkout or go to bed, a low-stock alert will encourage them towards a purchasing decision.
The point is that this tactic allows bespoke messages to be crafted to individual browsing patterns at breakneck speed.
This doesn't just work in theory; brands that have adopted composable CDPs are reaping unbelievable rewards. For instance, an electronics giant recently approached Syntasa to implement dynamic personalization, displaying relevant offers based on user behavior. Targeted product banners ensured visitors saw promotions matching their interests, enhancing engagement. For those who abandoned their carts, nudges re-engaged them upon return, encouraging purchase completion. This strategy helped to drive $6.4M in additional sales from "continue shopping" and cart nudges and recovered $100K+ in abandoned cart revenue in two quarters across key regions.
Similarly, for businesses such as a UK electronics retailer, the introduction of a real-time personalization engine on top of a warehouse-native CDP resulted in a £20M revenue uplift and £45M+ recuperation from cart abandonment campaigns.
## The Three Enablers of Successful Cart Nudges
Part of the magic enabled by an AI-backed composable CDP lies in its capacity to be laser-focused on the character of individual browsers, as well as on their behavior in the moment. The formula looks like this:
> Granular Segmentation + Real-Time Delivery + [AI-Driven Orchestration](/insights/ai-module-customer-lifecycle) = Uptick in Conversions
Without all of these elements, nudges are generic. With them, they become conversion engines.
## Why Retailers Are Choosing Composable CDPs
The UK retail market is competitive, margins are tight, and acquisition costs are climbing. Retailers need conversion to win that compound.
A [composable CDP](/insights/first-party-mode-composable-cdp) delivers:
- **Lower cost of conversion** – More recovered revenue with less wasted ad spend.
- **Future-proof personalization** – AI models plug directly into the data warehouse.
- **Trust and compliance** – [First-party mode](/insights/first-party-mode-composable-cdp) ensures retailers fully own their customer data.
## The Bottom Line
Cart abandonment isn't going away, but it doesn't need to be a sunk cost. With AI-powered nudges and a composable CDP, UK retailers are proving that even small percentage improvements add up to millions in recovered revenue.
The playbook is clear:
- Invest in an [anti-cart abandonment CDP](/enterprise) built on your own warehouse.
- Deliver AI-driven nudges in real time.
- Use social proof ecommerce tactics to build trust and urgency.
Do this, and more shoppers will go from casual browsers to loyal buyers.
---
# Why Retailers Are Choosing Composable CDPs for Personalization and Loyalty
URL: https://syntasa.com/insights/composable-cdp-retail-personalization-loyalty
Date: 2025-10-23
Category: POV
Audience: Enterprise
Overcome retail data fragmentation, connect first-party data for real-time personalization, build lasting loyalty with AI insights, and enable omnichannel engagement.
Each day, our expectations of brands go up. Without necessarily realising it, we want our shopping experiences to feel more uniquely customised – not to mention more instantly gratifying. In order to do this, marketers use data lakes and warehouses to store vast amounts of consumer data – from search behaviours to location to clicks – that allow them to build detailed customer profiles. However, despite the wealth of information, retailers still struggle to stitch together data from ecommerce, loyalty programs, CRM, and point-of-sale (POS) systems to form a truly hi-res view of individual customers and their online behaviour.
## The Retail Data Challenge: Fragmentation Meets Fatigue
When each platform holds only part of the picture, it is hard to fully understand the customer, let alone personalize at scale. Fragmentation means lost sales, wasted marketing spend, and weakened loyalty. Customer data platforms (CDPs) have greatly assisted businesses to aggregate their disparate consumer data, but they are already becoming a dinosaur that doesn't adequately deliver on promises of speed or personalization. Add in the [loss of third-party cookies](/insights/first-party-mode-composable-cdp) and stricter privacy laws and you have a 'solution' that is not just rigid and slow to deploy, but sometimes ends up creating new silos instead of breaking down old ones. Hence, more than half of retailers say they have real-time data but can't act on it fast enough.
The truth is that personalization isn't powered purely by masses of consumer information; it's powered by connected, first-party data. This is why traditional CDPs are increasingly being dumped by businesses, and replaced by more powerful and agile Composable CDPs.
## Powering Personalization and Loyalty with First-Party Data
Composable CDPs rely on unified customer profiles built from transactional, behavioral, and loyalty data. With those profiles, AI and predictive modeling enable offers or content to adapt in real time. You can recognize moments to cross-sell or flag churn risks early, and act accordingly – quickly and at scale.
For instance, Syntasa clients that used personalized recommendations saw a 3x add-to-basket rate increase within twelve weeks, on average, proving that when personalization runs on real-time data, loyalty stops being a program and becomes part of the relationship instead.
## The Loyalty Payoff: Personalization that Lasts
Predictive models let you personalize not just what rewards are offered but also when. Behavioral insights connect loyalty points or status tiers with real engagement metrics, not just purchases. Adaptive cart recovery and offers tuned to loyalty tiers help businesses retain customers rather than just focusing on chasing new ones.
## Omnichannel Engagement Without Complexity
Composable CDPs allow retailers to orchestrate experiences across email, SMS, push, paid ads, and in-store channels, all driven from unified real-time data. Marketers can use tools like drag-and-drop interfaces so they are less dependent on IT.
Moreover, compliance is built in: GDPR (General Data Protection Regulation in Europe), CCPA (California Consumer Privacy Act), and SOC 2 (Service Organization Control 2) are among the standards Syntasa meets, ensuring data protection and privacy as you scale.
## Why Retailers Are Making the Switch Now
In 2025 the priorities are clear: data ownership, AI activation, measurable loyalty. The move away from reliance on third-party cookies is accelerating. Brands like [Lenovo, Currys, and Sky](/customers) are already using Syntasa to get strong personalization ROI.
Retailers are realizing that the cost of inaction is now higher than the cost of change. Legacy CDPs can't keep up with modern personalization needs, particularly when consumers expect contextual relevance in real time. Composable CDPs give marketing and data teams full ownership of their tech stack, allowing them to integrate, test, and iterate faster without waiting on long IT cycles. This agility has made them the natural choice for retailers focused on measurable growth.
Equally important is the shift from reactive to predictive engagement. Instead of responding after a cart is abandoned, retailers can anticipate which customers are most likely to churn or convert and intervene earlier in the journey. That predictive capability is what drives consistent ROI and higher lifetime value – the benchmarks most retail executives are now judged against.
As data privacy rules tighten, composable architectures also help businesses to be future-proof. They make it possible to keep customer data in existing secure environments while still enabling AI and analytics to run at speed. For global brands operating across multiple jurisdictions, that flexibility is not just convenient; it's essential.
## Getting Executive Buy-In to Modernize Your Personalization Stack
Securing executive support for modernization can be tricky. Many leadership teams are wary of yet another martech investment after seeing mixed results from previous platforms. Others may worry that a new architecture will be disruptive or hard to justify against short-term targets. The key is to shift the conversation away from technology features and toward measurable business outcomes; i.e., efficiency, compliance, and revenue growth. When executives can see a clear financial and strategic upside, resistance typically fades.
If you're thinking of upgrading, one way to approach the conversation is to clearly articulate the problem and the solution. Below we've created a mini playbook that might help you secure executive support:
1. **Quantify the Problem** — Show the metrics: lost revenue from abandoned carts, wasted spend from undifferentiated promotions, discounts used unnecessarily, churn.
2. **Build a Quick Proof of Concept (POC)** — Run a test for cart recovery or a live recommendation widget on the website. Use your first-party data. Measure incremental lift or reduction in discount dependency.
3. **Align to Priorities** — Frame your argument in business and strategic terms: revenue growth, compliance, cost savings.
4. **Benchmark Proof** — Show what similar retailers are achieving: for example, "3× add-to-basket" increases, revenue uplift from cart recovery campaigns, competitive case studies.
5. **Package the Pitch** — Create a short Exec Brief or ROI Deck with:
- Business impact of current gaps.
- POC results.
- Competitive benchmarks.
- A 3-phase roadmap (Pilot – Scale – Roll-out).
- Summary of ROI and compliance benefits.
## Conclusion
Composable CDPs are no longer just an alternative; they're the foundation of modern retail personalization. They unify first-party data, activate AI in real time, and enable omnichannel engagement with less complexity and more control. The retailers adopting them today are the ones setting new standards for loyalty, speed, and customer experience tomorrow.
Explore how [Syntasa's Composable CDP](/#brief) helps retailers unify customer data, personalize every interaction, and drive loyalty that lasts.
[Explore the Syntasa Platform](/platform) | [View Case Studies](/customers) | [Book a Demo](/#brief)
---
# Syntasa Replaces Databricks for Faster, Smarter Data Activation
URL: https://syntasa.com/insights/syntasa-replaces-databricks-for-faster-smarter-data-activation
Date: 2025-09-25
Category: NEWS
Audience: Mission
Syntasa Replaces Proprietary Spark Solution with Best-of-Breed Approach
Syntasa has delivered its powerful data and AI platform to a U.S. agency, transforming the way it analyzes massive amounts of data and gains critical insights. The agency, which employs thousands of personnel, including civilians, military members, and contractors, sought a big data tool with an Authority to Operate (ATO) to efficiently process vast datasets in their Commercial Cloud Enterprise (C2E) environment.
The Syntasa platform provided the answer, allowing analysts and agency personnel to easily create complex analyses, visualize them on dashboards, and improve their datasets for analysis. These capabilities take advantage of native cloud services to scale to the needs of our customers, including:
- One customer in production with over 1 billion records at 2 terabytes per day
- A second customer in production with over 15 billion records at 1.5 terabyes per day
- A third customer in production streaming 40,000 records per day at 40 megabytes per second
- A benchmark showed 15% improvement when compared to proprietary Spark
In a side-by-side comparison, Syntasa was evaluated for Data Engineering, Data Science, and Analytics workloads. Many use cases were validated, including: natural language processing (topic modeling, sentiment analysis, entity extraction), geospatial (mobility data, trip & proximity analysis (H3), Automatic Identification System), and image processing (facial recognition, object detection).
This innovative approach has delivered significant benefits, including:
- Cost Transparency: the platform provides granular insight into cloud processing costs for each job, allowing for reporting at the user or group level, as well as guardrails and alerts when usage exceeds predefined thresholds. These capabilities allowed the agency to significantly lower the cost of their workloads.
- Intuitive User Interface: Analysts can now build their own workflows without writing any code.
- Enhanced Decision-Making: Dashboards provide a visual understanding of data, helping users make faster and more informed decisions.
- Seamless Automation: The system automatically incorporates new data and refreshes all datasets and dashboards.
> Jay Marwaha, Syntasa's Founder and CEO, shared his perspective: "We are proud to have partnered with this agency to bring our advanced data and AI capabilities to their mission. Our platform's ability to handle complex, large-scale data challenges and provide actionable insights in a user-friendly way demonstrates our commitment to empowering our customers to achieve their missions."
To learn more about how we can help your organization, [connect with our team](/#brief) to request a briefing.
---
# AI That Works Across the Entire Customer Lifecycle
URL: https://syntasa.com/insights/ai-module-customer-lifecycle
Date: 2025-09-04
Category: NEWS
Audience: Enterprise
From first-page personalization to long-term retention, Syntasa's AI Module delivers out-of-the-box models, DIY flexibility, and secure generative AI—powered entirely by your data.
For years, the promise of AI in marketing has been tempered by reality. Long implementation cycles, black-box SaaS platforms, and generic models that never quite fit your business have left many teams underwhelmed.
What's missing isn't more hype, it's AI that works today: fast to deploy, easy to tailor, and designed to keep your data safe.
That's why we built the Syntasa AI Module, part of our Composable CDP. It turns customer data into intelligent action across the entire lifecycle—without the complexity.
## What Is the Syntasa AI Module?
The AI Module combines three powerful capabilities in a single package:
- **Prebuilt Templates** — Enterprise-tested models for common use cases like cart abandonment, purchase propensity, or churn risk. They're fine-tuned to your business in as little as 1–2 weeks, so value shows up in days, not quarters.
- **DIY AI** — Direct access to your unified customer dataset inside your warehouse. Data scientists can build, train, and deploy custom models in their preferred ML stack (PyTorch, TensorFlow, scikit-learn, and more) with no messy extracts or sync jobs.
- **Generative AI, Without the Risk** — Apply GenAI to your own data whether it's content generation, analysis acceleration, or personalization securely inside your cloud environment, so not a single byte leaves your VPC.
It's AI that works out-of-the-box, but never locks you into someone else's box.
## AI in Action: From First Touch to Long-Term Loyalty
Every customer journey has moments that matter. The Syntasa AI Module provides models that learn from your data and adapt to each stage:
- **First Page Load** — Cohort Classifier instantly personalizes the very first experience for a new visitor.
- **Browsing Stage** — On-the-Fence Propensity detects intent surges or dips and delivers the right nudge at the right time.
- **Product View** — Category & Price Affinity reorders recommendations and promotions to match taste and budget.
- **Cart Stage** — Abandon-Risk Predictor identifies hesitancy in milliseconds and triggers save-the-sale tactics.
- **Post-Purchase** — Repeat-Buy & Bundle Recs suggest timely add-ons and replenishments that lift LTV.
- **Retention & Reactivation** — Churn-Risk Detector flags dormant profiles and launches win-back journeys.
Out-of-the-box models mean you can activate this intelligence from day one. DIY AI and GenAI let you extend it even further, adapting the module to your unique strategies.
## Spotlight: Cart Abandonment Risk Identification (New Beta)
We're excited to announce the private beta launch of our newest out-of-the-box Customer AI model: Cart Abandonment Risk Identification.
Cart abandonment is one of retail's biggest pain points, with average rates nearing 70% and representing billions in unrealized revenue. Our new model analyzes real-time behavioral, transactional, and contextual signals to predict abandonment risk early in the session—before the sale is lost.
With these predictions, retailers can trigger targeted in-session actions like:
- Dynamic offers that reduce friction
- Personalized messaging that re-engages the shopper
- Save-the-sale tactics that move customers toward checkout
The Cart Abandonment model joins other proven OOB templates such as Purchase Propensity, Product Recommendations, and Customer Reactivation Likelihood—giving retailers fast access to enterprise-tested models without the heavy lift.
Currently available in private beta with select customers, this model will be generally available soon as part of our in-session marketing packages.
## Why Syntasa AI Is Different
- Only CDP with full DIY AI capabilities
- Richer datasets, unifying every online and offline touchpoint into one view
- Native cloud integration with the AI platforms you already trust—Google Cloud AI, Azure ML, and more
- Designed for marketers and data scientists alike
Every model is prebuilt, custom, or GenAI-enhanced, which can instantly activate across your channels. Segment, personalize, and optimize without leaving Syntasa.
## Frequently Asked Questions (FAQ)
### What is a Composable CDP?
A Composable CDP (Customer Data Platform) is a modular approach to building customer data solutions. Instead of relying on a single vendor's monolithic platform, Syntasa lets you compose the capabilities you need—data ingestion, identity stitching, activation, AI—directly on your cloud. This gives you flexibility, control, and scalability without vendor lock-in.
### How is Syntasa different from a traditional CDP?
Traditional CDPs require you to move and store your customer data in their platform. Syntasa works natively within your existing cloud environment (Google, AWS, Azure), eliminating data duplication and keeping your data secure while reducing costs.
### What is the Syntasa Composable CDP AI Module?
The AI Module is part of Syntasa's Composable CDP. It combines prebuilt templates, DIY AI, and secure generative AI in one package, helping both marketers and data scientists turn customer data into intelligent actions across the lifecycle.
### How is this different from other CDPs or AI tools?
Unlike black-box SaaS platforms, Syntasa runs natively in your cloud. That means no extra data copies, no lock-in, and full control over your models, data, and costs.
### What kinds of use cases does it support?
From first-page personalization to long-term retention: cart abandonment prediction, churn detection, purchase propensity, category affinity, product recommendations, bundle suggestions, and more. Plus, DIY AI and GenAI let you extend to virtually any use case.
## The Bottom Line
AI doesn't need to be experimental, slow, or risky. With Syntasa's AI Module, you get a faster, safer, and more flexible way to put customer data to work powering personalization, predictions, and performance across the entire lifecycle.
---
# We Were Composable Before It Had a Name
URL: https://syntasa.com/insights/composable-before-it-had-a-name
Date: 2025-09-03
Category: POV
Audience: Enterprise
Composable CDPs represent the shift enterprises have been waiting for, a modular, warehouse-native architectures that put data back under the business's control. Syntasa was architected this way from the start, proving that composability isn't just a trend but a foundation for scalable activation and AI-driven personalization. The future of customer data will be built on these principles.
"Composable CDP" may be one of the hottest topics in martech today, but for us it's nothing new.
The term was only coined in 2022, but Syntasa had been delivering the same core principles for years before that: modular, warehouse-native capabilities designed to activate first-party data directly inside your own cloud. We weren't chasing a trend, we were building the architecture enterprises needed before the industry even had a name for it.
## The Industry's Long Road to Composability
The concept of a Customer Data Platform (CDP) isn't new, CDP first entered the industry vocabulary in 2013. But for nearly a decade, the dominant model was a monolithic SaaS platform, where data had to be moved, duplicated, and often surrendered to an outside vendor.
In 2022, "Composable CDP" emerged as the next generation: modular, cloud-native, and designed to run where the data already lives. Vendors have since been racing to adapt their products to this architecture. But for us, this wasn't a pivot, it was our foundation.
## Building Composable Before It Had a Name
Syntasa began delivering full CDP capabilities in 2020, and from the beginning, they were inherently composable.
Our architecture was designed to solve real-world enterprise challenges: complex data ecosystems, stringent compliance requirements, and the need for faster time-to-value. By running natively inside Google BigQuery, Snowflake, or a private cloud, Syntasa lets teams unify, enrich, and activate data without switching clouds, waiting on IT, or handing over control to a black-box vendor.
## Proof That Composable Works at Enterprise Scale
Our customers' results speak for themselves.
> "Syntasa has been invaluable in accelerating our time to value, structuring Adobe Analytics data and operationalizing data science and ML at scale. We can now confidently push insights into production to drive the user experience."
>
> — Tushar Mukherjee, Head of Global e-Commerce Analytics, Lenovo
Enterprise-Grade Impact:
- $300M+ in incremental revenue from first-party data activation
- 10M+ records processed daily
- £20M in website personalization revenue
- £45M+ recovered through cart abandonment programs
- 2× increase in campaign conversion rates
- Zero-copy activation pipelines for privacy and speed
## Market Validation: The Industry Catches Up
Today, "Composable CDP" is seen as the way forward. Analysts are talking about it, vendors are re-architecting their products, and enterprise buyers are prioritizing composability in their RFPs.
The market is validating what we've always known: monolithic isn't the future. The flexibility, control, and speed of composable architectures are no longer a differentiator, they're becoming the standard.
## Looking Ahead: Composable AI for the Next Five Years
We're continuing to innovate in the composable space, embedding AI capabilities directly into the activation layer so teams can move even faster:
- **Email Personalization Agent** – Auto-generates subject lines and copy from live profile data.
- **Conversational Insights Agent** – Answers natural-language questions like "Why did churn spike last quarter?"
- **Audience Creation Agent** – Translates plain-English prompts into SQL-grade audience definitions in seconds.
And we're deepening our alignment with Google Cloud to help GCP-native organizations activate first-party data directly in BigQuery, with no vendor lock-in.
## The Future Was Always Composable
For Syntasa, composable isn't a pivot, it's the model we've championed from day one. While the market works to catch up, we'll keep building the tools that give enterprises more control, more speed, and more impact from their data.
If your roadmap includes first-party data activation, privacy-first personalization, or AI-powered marketing, now is the time to make composable a reality.
---
# Syntasa Introduces Tech Preview for New Customer AI Model: Cart Abandonment Risk Identification
URL: https://syntasa.com/insights/cart-abandonment-risk-identification-tech-preview
Date: 2025-09-03
Category: NEWS
Audience: Enterprise
Syntasa is excited to announce the Tech Preview launch of our latest out-of-the-box (OOB) Customer AI model, Cart Abandonment Risk Identification, which is designed to help retailers detect when a shopper is likely to abandon their cart and act before the sale is lost.
It is a well-known statistic that cart abandonment rates in retail are quite high, averaging around 70–80% depending on industry. This means only about 3 in 10 visitors complete their purchase, representing billions in lost revenue. Syntasa's newest model in its Customer AI suite starts analyzing behavioral, transactional, and contextual signals in real-time to predict abandonment risk – before the customer has added any products to their cart. Retailers can use these predictions to trigger in-session actions, from a range of dynamic offers, to personalize messaging that keeps customers engaged and moving toward checkout.
## Part of the Syntasa Customer AI Suite
This newest model is part of Syntasa's AI module, a core part of our Composable CDP that delivers:
- **Out-of-the-box models** – like On-the-fence detection, Category affinity and Price Preference detection
- **Prebuilt Templates** — Ready-to-use model templates for common use cases that can be calibrated to your business in as little as 1–2 weeks.
- **Do It Yourself (DIY) AI** — Provides your data science team with full access to your unified online + offline dataset in your warehouse for custom model building in your preferred ML stack.
- **Generative AI** — Secure, cloud-native GenAI capabilities that keep your customer data private while unlocking new personalization and analysis opportunities.
From the moment a visitor lands on your site to years after their first purchase, Syntasa's Customer AI is built to predict, personalize, and perform at every stage of the customer lifecycle. The Cart Abandonment model joins other proven OOB templates such as Purchase Propensity, Product Recommendations, and Customer Reactivation Likelihood, giving retailers fast access to enterprise-tested models without the heavy lift.
## Availability
The Cart Abandonment Risk Identification model is in tech preview with select customers today, and will be released more broadly soon.
## Explore More
- [View our Google Cloud Marketplace listing](https://console.cloud.google.com/marketplace/product/syntasa-public/syntasa-composable-cdp)
- Discover how Syntasa's Composable CDP Fuels Global Electronic Retailer's Digital Evolution
- [Explore all Customer Success Stories](/customers)
---
# First Party Mode: How Syntasa's Composable CDP Enables True Data Ownership & Activation
URL: https://syntasa.com/insights/first-party-mode-composable-cdp
Date: 2025-08-19
Category: POV
Audience: Enterprise
As browsers block third-party tags and ad blocker usage surges, enterprises are losing critical behavioral insights. Syntasa's Composable CDP with First Party Mode restores visibility by collecting and activating data directly under your domain and within your cloud—secure, compliant, and vendor-agnostic. This shift gives marketers the precision, agility, and control they need to personalize at scale without sacrificing trust or performance.
With the phase-out of [third-party cookies](https://www.invoca.com/blog/tracking-cookies-are-dead-what-marketers-can-do-about-it) and the rise of tracking protections, marketers have lost visibility into much of the customer journey. Safari and Firefox led the charge years ago, and now Chrome is catching up. Add in device-level privacy features and ad blockers, and even the best digital stacks are struggling to deliver the full picture.
Most analytics tools still look like they're working. But behind the scenes, they're missing critical segments, especially among users on iOS, Safari, or privacy-first sessions. This isn't a future concern; it's happening now, and it's impacting every enterprise trying to make data-driven decisions.
Google's Tag Gateway (formerly First-Party Mode) lets you serve Google tracking scripts from your own domain, which is a helpful step forward in privacy and reliability. But it's limited to Google's tools. Syntasa's Composable CDP goes further: RT-tag captures behavioral data directly under your domain, processes it in your cloud, and activates it across any channel without third-party dependencies.
## Modern Browsers are breaking legacy tools
Most traditional analytics and personalization platforms rely on third-party domains to collect data. But in today's privacy-first environment, that approach no longer holds. Modern browsers are quick to flag or block those tags—even before a single event is collected. And that's not all: ad blockers compound the issue. As of early 2025, approximately 32.5% of global internet users use ad blockers at least some of the time, including over 900 million people worldwide, with mobile users making up 54% of that total. In the U.S., usage hovers around 32.2%, and it's even higher among technically savvy users—surging up to 52% of Americans in some surveys.
Even platforms like GA4 or Adobe can miss entire sessions when requests come from untrusted sources. It doesn't matter how well you've implemented them if the browser doesn't trust the tag, tracking fails.
And when tracking fails, everything downstream suffers. Personalization doesn't trigger. Journeys fragment. Optimization stalls. Marketing strategies end up running on partial data and stale assumptions driving up costs while eroding performance.
Cookie deprecation isn't coming. It's here. And for many brands, the impact is already being felt.
## What First Party Mode Does Differently
First Party Mode isn't a patch, it's a fundamental shift. It moves your data collection and activation to your own subdomain, running entirely from your cloud environment. To the browser, it looks and behaves like your site, because it is.
There are no redirects, no third-party domains, and no signals of external ownership. It runs cleanly across Chrome, Safari, Firefox, and other modern browsers. And most importantly, it all happens post-consent. That means your data collection respects both browser restrictions and regulatory requirements by design.
## See What You've Been Missing
When tracking fails, most systems don't raise a flag. There's no alert. No error. Sessions drop quietly, often before a single event is captured. The result: gaps in behavioral data that go unnoticed but compound over time.
First Party Mode changes that. By running directly under your domain, it restores visibility into previously blocked sessions—ethically, securely, and with full respect for user consent. These behavioral signals, once collected, are immediately available for activation inside your cloud using your existing models and logic. No third-party routing. No data handoffs.
## Built for Control. Designed for Compliance.
The difference isn't just in what First Party Mode captures—it's where it runs.
Most CDPs are SaaS platforms, meaning your data is routed through vendor infrastructure and third-party domains. Modern browsers treat that as external—and block accordingly.
Syntasa's Composable CDP is cloud-native and fully deployable within your environment—GCP, Azure, AWS, or a mix of cloud and on-premises infrastructure. Your data stays in your infrastructure, governed by your policies, and activated without duplication, delay, or risk of leakage.
## In Practice
A leading UK electronics retailer realized that despite using major analytics platforms, key behavioral signals were missing—particularly across Safari and mobile. It wasn't a misconfiguration. It was a modern browser doing exactly what it was designed to do: block third-party tags.
After adopting Syntasa's First Party Mode, sessions that were once invisible—cart interest, promo engagement, delivery slot activity—became observable again. With real-time data flowing through their own cloud, teams could act faster and personalize more effectively. Implementation was direct. No workaround logic, no external tag orchestration—just clean, compliant data collection.
## A Quiet, but Strategic Shift
First Party Mode often flies under the radar. It's not a standard RFP checkbox, and few teams are evaluating CDPs based on cloud architecture or browser enforcement.
But once they understand what they're missing and what a first-party, cloud-native setup can unlock, it quickly becomes a strategic priority.
This isn't about circumventing privacy. It's about aligning with it. First Party Mode works because it respects browser boundaries and honors user consent while still delivering the behavioral data today's organizations need to move fast and operate with precision.
## Reclaim Control
The days of reliable third-party tracking are over. But that doesn't mean your visibility, activation, or control needs to suffer.
With Syntasa's Composable CDP and First Party Mode, you collect first-party data from your own domain, process it securely in your own cloud, and activate it on your terms. No duplication. No vendor lock-in. No blind spots.
That's modern data ownership by design.
[Ready to see it in action? Let's talk about what First Party Mode looks like in your environment.](/#brief)
---
# Syntasa Launches Composable CDP on Google Cloud Marketplace
URL: https://syntasa.com/insights/syntasa-composable-cdp-google-cloud-marketplace-launch
Date: 2025-08-12
Category: NEWS
Audience: Enterprise
New Listing Enables Faster Deployment of Composable CDP for Google Cloud Customers
Syntasa, offering both a Data & AI Platform and a Composable CDP for enterprise marketing teams, announced that its Composable CDP is now available on the [Google Cloud Marketplace](https://console.cloud.google.com/marketplace/product/syntasa-public/syntasa-composable-cdp). This listing allows Google Cloud customers to easily deploy and scale Syntasa's modular customer data platform within their existing cloud infrastructure, eliminating procurement barriers and accelerating time to value.
## Unlocking Modern Data Activation
As marketers face growing pressure to deliver personalized experiences using first-party data, traditional CDPs often fall short, either too rigid to fit existing stacks or too slow to deploy. Syntasa's Composable CDP breaks that mold, offering a flexible, component-based architecture that works with data where it already lives.
Now available through the Google Cloud Marketplace, Syntasa's Composable CDP helps teams:
- Rapidly build and activate dynamic customer segments using data in BigQuery and other GCP tools
- Run next-best-action and personalization models in real time
- Avoid the cost, complexity, and vendor lock-in of all-in-one CDPs
- Meet procurement and security requirements with data that never leaves your Google Cloud environment
## Inside Syntasa's Composable CDP
Syntasa's Composable CDP is designed for modern marketing and analytics teams that want to activate first-party data without compromising flexibility or speed. Its core modules, Audiences, Data Ready, Core, and AI, can be deployed individually or together, allowing teams to design a CDP that fits their goals.
Unlike legacy platforms, Syntasa is:
- **Composable** – Choose only the capabilities you need
- **No-code** – Empower marketing teams to build, activate, and iterate without relying on engineering
- **Real-time** – Trigger personalized offers and messaging in milliseconds
## Ready to Get Started?
Syntasa's Composable CDP is now live on the [Google Cloud Marketplace](https://console.cloud.google.com/marketplace/product/syntasa-public/syntasa-composable-cdp). Organizations already using Google Cloud can deploy directly from the listing, or schedule a personalized walkthrough with our team to learn more.
---
# Syntasa + Google Cloud: Five Years of Partnership, Five Years of Proven Impact
URL: https://syntasa.com/insights/syntasa-google-cloud-five-years-partnership
Date: 2025-08-01
Category: POV
Audience: Mission, Enterprise
Over the past five years, Syntasa and Google Cloud have partnered to deliver powerful, production-grade analytics and AI solutions across government, healthcare, and commercial sectors. From GenAI-powered behavioral health apps to Composable CDPs for retail and higher education, this collaboration proves what's possible when innovation meets real-world impact. With deep GCP integration and a cloud-native approach, Syntasa continues to turn data into outcomes at scale.
## A Milestone Worth Celebrating
Five years ago, Syntasa began a collaboration with Google Cloud aimed at helping data-rich public organisations turn insight into action. Today, that partnership encompasses everything from generative AI apps for behavioral health to enterprise-grade Composable Customer Data Platforms (CDPs). Syntasa has become a formidable solution partner for Google and our joint customers.
## Where It All Began
That very first lightning bolt struck in late 2019 when we met a Customer Analytics lead from Google Cloud Public Sector, and they shared a few of the persistent pain points they were seeing in government digital programs. The resulting proof-of-concepts for Oklahoma and California quickly validated the synergy between Syntasa's product and know-how and Google Cloud's data and analytics technology.
## Partnership Timeline at a Glance
## Signature wins that defined the journey
### Oklahoma Department of Mental Health & Substance Abuse (ODMHSAS) – Improving Substance Abuse Outcomes with Analytics
Harnessing real‑time analytics, Syntasa teamed up with Google Cloud to help ODMHSAS unify data across digital interactions, health outcomes, and community behavior delivering granular insights that optimize opioid prevention and treatment strategies. The platform enables faster, smarter outreach by blending web and app activity with overdose, treatment, and data to improve prevention and treatment services with modern data analytics. [Learn more](/customers).
### AHCCCS (Arizona) – GenAI Provider Locator
A conversational, Gemini-powered locator helps residents find opioid-treatment services in seconds, reducing access-to-care friction statewide. [Learn more](/customers).
### California Office of Digital Innovation (ODI)
The California Office of Digital Innovation partnered with Syntasa and Google Cloud to collect and analyze over 90,000 quantitative and 80,000 qualitative responses via sentiment analytics, producing zip‑code‑level insights into vaccine willingness and hesitancy that informed equitable distribution, targeted outreach, and mobile clinic deployment across the state. [Learn more](/customers).
### United Kingdom Cabinet Office and California Office of Emergency Services – COVID-19 Vaccine Insights
Surveys, search, and website analytics surfaced attitudes and barriers to vaccination, informing public health outreach during the crucial 2020-2021 rollout of COVID-19 vaccines. [Learn more](/customers).
### Federal Government Agency – Public-Comment Integrity
Our Sentiment Analytics solution, including Large Language Models, was deployed within six weeks in a FedRAMP Medium environment on Google Cloud, and identified millions of duplicate and near-duplicate comments. [Learn more](/customers).
## Why The Partnership Works
- Cloud native architecture – Syntasa solutions run within the customer's Google Cloud account, aligning with internal security controls.
- Pre-built Solutions – Templates and accelerators for Substance Abuse, Sentiment Analytics, Journey Analytics, and DonorAI cut time-to-value to mere weeks.
- Tight Collaboration – Syntasa has tight integration with Google Cloud Sales, Engineering, Delivery, and Support teams, ensuring we are aligned to deliver value to our joint customers.
## Looking To The Future
The Syntasa team is incredibly excited about our opportunity to grow and develop our partnership with Google Cloud. We a building on our momentum in Public Sector in several ways.
- Commercial expansion: Retailers wrestling with SaaS CDPs can modernise on a composable stack running on world-class data and AI technology from Google Cloud. Early pilots in aviation and specialty retail are underway, in collaboration with our partners at Google's Cloud.
- Higher Education: Fundraising and advancement teams can capture unrealized value from the mountains of data they have on their donors
- Technology advancement: Google Cloud is leading the industry with incredible new capabilities in data and AI, and Syntasa is constantly finding ways to include them in our offerings. Generative AI-first modules for our composable CDP and natural language analytics capabilities are two examples.
- Expanded marketplace footprint: Adding our new Composable CDP listing to our existing Data + AI Platform listing makes it easier for our joint customers to get started and get value quickly.
## Looking To The Future
Whether you're a government or private sector organization looking to get more value from your investment in Google Cloud or Googler looking to bring innovative solutions to your customers, our team is ready to help. Explore the resource library or reach out at [info@syntasa.com](mailto:info@syntasa.com).
Because when you combine Google Cloud's scale with Syntasa's speed, data stops being a cost center and starts driving outcomes.
---
# Audience Building Isn't Dead—You're Just Doing It Wrong
URL: https://syntasa.com/insights/audience-building-composable-cdp
Date: 2025-07-30
Category: POV
Audience: Enterprise
Audience building isn't dead, it's just broken. Traditional segmentation relies on outdated data, manual rules, and siloed systems that can't keep up with modern consumer behavior. A Composable CDP flips the script, enabling marketers to build dynamic, predictive audiences using real-time data and governed infrastructure that actually works.
Marketers love to declare things "dead." And audience building? It's been on the obituary list for years.
We've heard it all: "Let the algorithm do the work." "Context beats targeting." "Users don't want to be followed." But here's the truth: audience building isn't dead—it's just broken.
It's not for lack of data. Or tools. Or engagement. We've got more signals, platforms, and user touchpoints than ever before. And the issue is not a want of user engagement – users are more present online and dialled into branded messaging than [at any other time](https://www.engagebay.com/blog/online-shopping-trends-statistics/#The_Rise_of_eCommerce_Online_Shopping_Statistics) in consumer history. So, why do so many brands still struggle to create effective, high-performing audiences?
The issue is in the infrastructure. Static segments, stale data, and manual guesswork plague traditional approaches, leading to wasted ad spend and missed opportunities.
And the solution isn't another siloed tool—it's a Composable CDP approach: one that leverages real-time data, model-driven logic, and warehouse-native control to build audiences that actually work.
## Why Traditional Audience Building Is Broken
Marketers today face a paradox: they have more data than ever, yet their audience segmentation remains frustratingly ineffective. The symptoms are easy to spot:
- Static segments based on rigid rules that don't reflect real-time behavior.
- Slow refresh cycles, where campaigns rely on last week's (or last month's) data.
- Generic personalization, despite promises of "hyper-targeting".
The root causes run deeper:
- **Poor data quality and governance** – Disparate sources, duplicate records, and inconsistent definitions that lead to unreliable segments.
- **Disconnected martech stacks** – Data trapped in silos preventing unified customer views.
- **Manual segmentation logic** – Marketers relying on guesswork rather than predictive insights.
In the context of an online ecosystem that is always evolving at an exponential rate, the outcome of all this is obvious: audiences are outdated before they're even activated.
## The Rise of the Composable CDP
The Composable CDP isn't another black-box platform—it's a flexible, warehouse-native approach that integrates with existing infrastructure. Unlike traditional Packaged CDPs that force data into proprietary environments, a Composable CDP operates directly on your cloud data warehouse, eliminating duplication and ensuring governance.
### Why It Matters for Audience Building
When applied to the conundrum of audience building, the Composable CDP simply rules the roost. Consider the advantages:
- **Real-time data access** – No more waiting for batch updates; audiences refresh as behaviors change.
- **AI/ML-driven logic** – Predictive scoring and clustering replace manual rules.
- **Full transparency and control** – Every segment is traceable back to its source data.
In other words, implementing a Composable CDP ensures audiences are built on clean, up-to-date data and seamlessly pushed to execution channels.
## Fixing Audience Building—What Actually Works
### Step 1: Start with Clean, Trusted First-Party Data
Audiences are only as good as the data behind them. A warehouse-native approach ensures segmentation happens where data is already governed—eliminating inconsistencies and duplication.
### Step 2: Make It Real-Time and Recurring
Behavioral signals decay fast. A customer who browsed laptops yesterday is more valuable than one who did so three weeks ago. By refreshing segments daily (or in real-time), brands ensure relevance.
### Step 3: Let Models Guide Your Targeting
Instead of manually defining segments, predictive models can:
- Score likelihood to convert or churn
- Cluster users by behavioral patterns
- Recommend next-best actions
This shifts marketers from operators to orchestrators, reducing dependency on data teams while improving precision.
## What It Looks Like in Practice
Let's take two real-world case studies – both clients of Syntasa – to see how this actually works.
### Moving a Global Electronics Brand from Manual Guesswork to Predictive Precision
Until recently, a leading electronics manufacturer relied on rigid, rules-based segments that were manually exported to media platforms. This process created delays—campaigns often targeted users based on behaviors that were days or weeks old. Additionally, their segmentation logic was simplistic, treating all "cart abandoners" or "product page visitors" as equally valuable, despite vast differences in purchase intent.
By implementing a Composable CDP with model-driven segmentation, however, the brand transformed its approach:
- Predictive scoring replaced manual rules, identifying users with the highest likelihood to convert based on real-time behavior, past purchases, and engagement patterns.
- Dynamic audience refreshes occurred daily, ensuring campaigns targeted users while their intent was still fresh.
- Automated syncs eliminated delays, pushing updated segments directly to activation platforms like Google Ads and Meta.
- Over $200M in incremental revenue
- $3M+ in revenue from in-session marketing campaigns
- Recovered $100K+ in abandoned cart revenue within one quarter across NA, EMEA, and APAC
- Generated $106M+ in incremental revenue from social proofing alone
### Eliminating Audience Overlap and Waste for an Electronics Retailer
A major electronics retailer was struggling with audience fragmentation, with different teams building overlapping segments containing conflicting rules, leading to inefficient media spend. For example, a single high-value customer might be targeted simultaneously as a "loyalty member", "discount seeker", and "high-intent browser", resulting in vastly redundant ad exposure.
After adopting a warehouse-native CDP, however, all customer data was consolidated into a single source of truth. As a consequence:
- Rules were standardized across teams, eliminating contradictions.
- The system adapted to flag users who qualified for multiple audiences, allowing marketers to prioritize the most relevant engagement strategy.
- Segments updated in near real-time, ensuring campaigns reflected the latest behaviors.
For the retailer, Syntasa automated audience segmentation and real-time activation across multiple platforms, driving £30M+ in additional revenue by improving marketing effectiveness and enabling dynamic retargeting. Moreover, the introduction of a Composable CDP improved cross-team collaboration—gearing marketing, analytics, and media teams to work from the same playbook.
## The Future of Audience Building Is Composable
Audience building isn't dying – it's evolving – and the marketer's role has shifted from manual segment wrangler to strategic orchestrator.
The problem was never audience building itself—it was the outdated, fragmented ways brands were forced to do it. Legacy tools and manual processes simply can't keep pace with real-time consumer behavior, leading to missed opportunities and wasted spend.
A composable CDP changes the game by combining:
- ✔ Clean, governed data
- ✔ Real-time refreshes
- ✔ Model-driven logic
The final result is that marketers can reliably keep up with delivering dynamic personalized campaigns that are reconciled with rapidly shape-shifting audience segments.
[If you are ready to move beyond broken audience strategies, let's talk.](/#brief)
---
# How to Turn Your Data Warehouse Into a Marketing Powerhouse
URL: https://syntasa.com/insights/data-warehouse-marketing-powerhouse
Date: 2025-07-29
Category: POV
Audience: Enterprise
Most brands are sitting on a goldmine of customer data in their warehouses—but without direct activation, it goes unused. This blog explores how Syntasa turns Snowflake, BigQuery, or Redshift into real-time marketing engines, eliminating the need for costly, outdated MarTech stacks. The result? Smarter segmentation, faster campaigns, and fewer data silos.
Most brands today have invested in modern data warehouses like Snowflake, BigQuery, or Redshift, yet few are leveraging these systems to their full marketing potential. While these platforms excel at storing and analyzing customer data, they often remain siloed from the marketing tools that could put this valuable customer data to work. Frequently, businesses wind up with bloated MarTech stacks that are slow, expensive, and disconnected from their most trusted data source.
With Syntasa, you can break this cycle by activating first-party data directly from your warehouse—turning what was once just a storage repository into a powerful marketing engine. It's time to evolve from simply "storing and analyzing" data to fully "storing, analyzing, and activating" it in real time.
## The Limitations of Traditional Martech Stacks
As we [may have mentioned before](/insights/composable-cdp-must-evolve-or-die), traditional marketing technology platforms were built for a different era—one where customer data was scattered across channels and needed centralized collection. For their time and place, these tools worked a charm; today they are an anachronism, and they are holding you back.
The fundamental issue lies in the data duplication these systems require. To power campaigns, customer data must be extracted from a warehouse, transformed, and loaded into traditional CDPs. This process isn't just time-consuming; it introduces risks. Each data transfer creates opportunities for errors, inconsistencies, and security vulnerabilities.
These limitations have real business consequences. By the time campaigns launch, the customer profile they're based on is already outdated, and personalization efforts fall flat because they're based on incomplete pictures of customer behavior. Meanwhile, teams waste countless hours reconciling data across systems rather than focusing on strategic initiatives.
## Your Warehouse: An Untapped Marketing Asset
The irony is that most organizations already have the solution to these problems sitting in their data warehouses. Modern platforms like Snowflake, BigQuery, and Redshift offer exactly what marketers need: scalable infrastructure, enterprise-grade governance, and centralized access to customer data. With the right layer on top—like Syntasa—these warehouses can be transformed into real-time marketing engines for unified customer profiles, predictive insights, and personalized activation.
To be clear: these warehouses already contain your most valuable first-party data, including purchase histories, product usage patterns, customer service interactions, and more; a veritable goldmine for marketers trying to deliver relevant experiences. Yet without a way to activate it directly, this data remains frustratingly out of reach for campaign execution.
The missing piece isn't more storage or processing power. What's needed is a bridge between your warehouse's analytical capabilities and your marketing execution channels—in other words, a way to turn insights into action without the overhead of traditional martech stacks.
## Activating Warehouse Data with Syntasa
This is where Syntasa changes the game. The platform enables true native data activation, using data in your warehouse to power audience segments, trigger personalized campaigns, and optimize customer experiences in real time, all without ever copying data out of your secure environment.
Syntasa connects directly to your existing warehouse, giving marketers a no-code interface to build segments and activate campaigns while maintaining all the governance and security your data team requires. Need to target high-value customers who haven't purchased in 30 days? The segmentation happens directly against your warehouse data, with results synced to your advertising platforms, email systems, or web personalization tools.
Some of the key advantages of this approach include:
- **Zero data duplication:** Your customer information stays securely in the warehouse, eliminating the risks and costs of data movement
- **Real-time responsiveness:** Campaigns can react to customer behaviors as they happen, not days or weeks later
- **Transparent operations:** Both marketing and data teams have complete visibility into how segments are constructed and used
Syntasa complements your existing investments, enhancing what your warehouse can do rather than replacing it.
## Getting Started with Warehouse-Powered Marketing
Fortunately, transitioning to a warehouse-centric marketing approach doesn't require ripping out your existing systems. For instance, a typical implementation path might look like this:
1. **Assess your current data:** Catalog what customer information already lives in your data warehouse and identify any gaps that need filling.
2. **Prioritize use cases:** Start with high-impact opportunities (e.g., abandoned cart recovery, customer win-back campaigns, or loyalty program engagement) where real-time data makes the biggest difference.
3. **Implement incrementally:** Connect Syntasa to your warehouse and begin with a pilot campaign to demonstrate value before scaling across more use cases and channels.
4. **Optimize continuously:** Use the feedback loop between campaign performance and warehouse data to refine segments and improve results on the fly.
The beauty of this approach is its flexibility. Teams can start small with a single use case while building toward a comprehensive warehouse-powered marketing strategy.
## Unlocking Your Warehouse's Potential
Your data warehouse represents one of your organization's most significant technology investments. With Syntasa, it can become your most powerful marketing asset too—eliminating data silos, reducing MarTech complexity, and enabling truly customer-centric campaigns.
The shift from passive storage to active marketing execution doesn't require overhauling your infrastructure. It simply requires connecting the dots between the data you already have and the campaigns you want to run. By activating your warehouse data directly, you can finally deliver the timely, relevant experiences customers expect while giving both marketing and data teams the tools they need to succeed.
[Ready to transform your data warehouse from a cost center to a competitive advantage? Let's talk about how Syntasa can help you start executing campaigns with the speed and precision your customers expect.](/#brief)
---
# How Composable CDPs are Powering In-Session Marketing
URL: https://syntasa.com/insights/composable-cdp-in-session-marketing
Date: 2025-06-20
Category: POV
Audience: Enterprise
Imagine this: You abandon a cart on an e-commerce site, and within seconds, a personalized discount appears—not just any offer, but one tailored to your past purchases, browsing behavior, and even real-time inventory levels. Elsewhere, a streaming service adjusts its homepage layout dynamically based on your mood, inferred from the types of trailers you've hovered over.
This isn't magic. It's in-session marketing, and it's reshaping customer expectations forever.
The rise of commercial AI such as ChatGPT, Midjourney, and GitHub Copilot is doing more than dazzle us with novelty—it is rewiring consumer expectations and demand. Hyper-personalized, real-time experiences are now table stakes. Just as the introduction of color TV made black-and-white obsolete virtually overnight, businesses stuck with rigid, legacy systems are already falling behind.
So how does all of this connect to CDPs? Very simple: traditional Packaged CDPs represent the past. They were good for their time, but today they are rigid, slow to adapt, and often disconnected from the new digital landscape being forged by AI. By contrast, Composable CDPs represent the future. They are the CDP system update required to take full advantage of the ground-shaking potential contained within cutting-edge ML models and AI. Sure, Packaged CDPs still work okay for now, but Composable CDPs provide by far the best foundation to drive AI-powered personalization at scale.
## What Is a Composable CDP?
Unlike traditional Packaged CDPs (all-in-one solutions with fixed architectures), a Composable CDP is built like a best-in-class tech stack:
- Modular by design, letting businesses plug in specialized tools for data ingestion, identity resolution, and real-time activation—while seamlessly integrating with your existing tech stack.
- Cloud-native and API-first, ensuring seamless integration with existing martech and AI ecosystems.
- Flexible and scalable, adapting as new AI models and data sources emerge.
Think of it like upgrading from a pre-built desktop to a custom gaming rig—you get higher performance, zero bloatware, and the ability to swap out parts as needed.
For a deeper dive, see our guide: Composable CDP: [The Future of Customer Data](/insights/composable-cdp-complete-guide).
## Why In-Session Marketing Demands Composability
So what gives a Composable CDP the edge over its packaged counterpart when it comes to in-session marketing? In a nutshell, it's all about how AI models access and learn from data. Composable CDPs deliver clean, complete, and current data—directly from your data warehouse—so AI models can perform at their best. Traditional CDPs, on the other hand, make it difficult to get data in and even harder to get data out. Their rigid architecture often creates bottlenecks, slows updates, and limits integration with broader analytics workflows. Most don't offer built-in modeling capabilities, meaning AI teams are stuck copying data into separate environments just to get started. The result? Fragmented data, stale insights, and underperforming AI. It's the old computing adage: garbage in; garbage out.
Traditional CDPs often force AI to work with:
- Stale data (batch updates, laggy syncs)
- Siloed fragments (disconnected touchpoints)
- Rigid schemas (hard-coded fields that can't evolve)
A Composable CDP fixes this by:
- Streaming real-time data directly within your data warehouse—so AI can act on fresh signals without delays or duplication (e.g., a cart abandonment triggers an offer before the customer leaves).
- Enabling custom ML models—like NLP (natural language processing) for parsing customer service chats or [computer vision](https://www.ibm.com/think/topics/computer-vision) for analyzing product engagement.
## Real-World Impact
Perhaps the key to understanding how Composable CDPs allow brands to get ahead of their competitors is best summed up in a single word: Speed.
With a Composable CDP:
- A retailer detects a high-value shopper browsing winter coats and instantly serves a VIP promo—powered by real-time signals processed directly within the data warehouse, without copying data to an external CDP or relying on rigid, pre-built workflows.
- A media company personalizes newsletter content in milliseconds—using real-time click data processed directly in their data warehouse. No syncing delays, no stale exports—just up-to-the-moment personalization powered by zero-copy architecture.
- A SaaS firm stops churn by triggering win-back flows the moment usage drops.
Traditional CDPs simply can't keep up. Their batch-processing pipelines introduce delays, while their inflexible schemas block AI from accessing critical context.
## Unlocking Hyper-Personalization at Scale
With speed, it's the shift from static segments to AI-generated micro-segments where Composable CDPs truly shine. Instead of relying on broad demographics (e.g., 'women aged 25–34'), AI dynamically clusters audiences based on real-time behavior—like 'users who browsed hiking gear, read sustainability blogs, and abandoned a cart on eco-friendly backpacks.'
What sets a Composable CDP apart is its ability to tap into the full power of your cloud environment—seamlessly integrating with modern AI/ML tools to drive personalization strategies. This unlocks a level of precision and agility that traditional CDPs—limited by proprietary models and siloed data—can't match.
The result? Context-aware messaging that adapts across channels—serving a mobile push notification about a restocked item, an email with complementary accessories, and a retargeting ad with a limited-time discount—all in perfect sync. Even better, AI feedback loops continuously optimize these interactions, tracking which micro-segments drive the highest lifetime value and refining campaigns on the fly.
## How Syntasa Makes It Happen
Syntasa's Composable CDP is the engine behind in-session marketing—combining real-time data orchestration with customizable AI pipelines to deliver results that legacy systems can't match.
Key Differentiators:
- **Private Cloud Integration:** Deploy securely in your cloud (AWS, Azure, GCP) with full data ownership—no third-party black boxes.
- **Tailored AI Workflows:** Build and fine-tune ML models for your exact use case (e.g., churn prediction, dynamic pricing, micro-segmentation).
- **Enterprise-Grade Governance:** Maintain compliance (GDPR, CCPA) while unifying data across silos for a single customer view.
- **Proven Impact:** One global electronics retailer used Syntasa's Composable CDP within their GCP environment to unify customer data across devices and sessions by stitching together fragmented interactions, creating 22 million Customer 360 records. Among other benefits, the retailer obtained £20M in revenue from website personalization and £45M+ from cart abandonment campaigns—all powered by real-time behavioral triggers. You can read the [full case study here](/customers).
## FAQs
### How is a Composable CDP different from a data warehouse or CDI (customer data integration)?
Data warehouses store but don't activate data; CDIs resolve identity but lack AI integration. A Composable CDP does both—plus it executes real-time personalization with built-in AI pipelines.
### Can we use our existing ML models with Syntasa?
Yes! Syntasa's open architecture lets you plug in proprietary models or leverage our library of pre-built algorithms (e.g., NLP for chat analysis, computer vision for product recommendations).
### Is composability only for enterprise-scale brands?
Not at all. While enterprises benefit most from scalability, mid-market brands gain agility—like A/B testing AI models without costly overhauls.
## The Bottom Line
AI isn't the future—it's the now. And legacy CDPs are becoming the bottleneck. Composable CDPs don't just support in-session marketing; they accelerate it. By delivering clean, real-time, unified data, they turn AI from a novelty into a revenue-driving engine.
The question isn't whether to upgrade—it's how fast you can adapt. [Enquire now to find out how Syntasa can help you make the change.](/#brief)
---
# Why a Composable CDP Is the Only CDP Built for GCP
URL: https://syntasa.com/insights/composable-cdp-built-for-gcp
Date: 2025-06-12
Category: POV
Audience: Enterprise
If your business runs on Google Cloud Platform (GCP), you likely already appreciate the value of keeping your data secure, scalable, and fully under your control. What you might not realize is that most Packaged Customer Data Platforms (CDPs) force you to copy your data outside of GCP and into a vendor-controlled environment where you lose visibility, governance, and efficiency.
That's where a Composable CDP changes the game. Unlike Packaged CDPs, a Composable CDP is built to live inside your GCP environment, leveraging your existing cloud infrastructure while maintaining full ownership of your data. If you want a CDP that truly harnesses GCP's power and operates within your cloud, composability isn't just an option—it's more like a necessity.
## The Benefits: Why GCP + Composability = Ideal Architecture
By deploying a composable CDP on GCP, businesses retain full ownership of their data while unlocking real-time personalizations, AI-driven analytics, and seamless integrations—all within their own cloud. The result is faster innovation, stronger governance, and a future-proof architecture.
The benefits?
- **Security** – No unnecessary data movement outside your controlled environment.
- **Compliance** – Fine-tuned access controls via GCP's Identity and Access Management (IAM).
- **Cost efficiency** – No vendor markup on storage or compute—just GCP's native pricing.
- **Scalability** – Direct integration with GCP's services means no bottlenecks.
## One Cloud to Rule Them All
Keeping all your customer data and analytics in your own cloud means avoiding reliance on a third-party SaaS provider to export your data into their ecosystem before generating insights. With a Composable CDP, everything runs directly within your existing data warehouse—enabling zero-copy data access and eliminating unnecessary duplication.
GCP is uniquely well-suited for this approach. Unlike traditional Packaged CDPs, which rely on extracting your data and locking it into a vendor's black box, a GCP-run Composable CDP takes advantage of Google's native infrastructure for CDP analytics—without having to extract the data first. Just like a Composable CDP, Google's cloud is modular, allowing you to choose best-of-breed tools (BigQuery for warehousing, Vertex AI for machine learning, Pub/Sub for event streaming etc.) and integrate them seamlessly under your own governance.
## Traditional Packaged CDPs Just No Longer Cut It
Most CDPs are built as monolithic SaaS platforms, forcing you to move your data into their environment—away from your governed, cost-optimized GCP infrastructure. This creates three critical problems:
- **Loss of Control** – When your data lives in a vendor's system, you lose visibility into how it's processed, who accesses it, and how compliance is enforced. If you're already plugged into GCP, this means that Google's granular IAM and audit logging essentially become irrelevant.
- **Unnecessary Costs** – SaaS CDPs charge premiums for storage and compute, even though you've already invested in BigQuery and GCP's scalable resources. Worse, you pay to move data out of your cloud and into theirs.
- **Reinforced Silos** – Ironically, while CDPs promise data unification, they often become yet another silo. Your data is trapped in a proprietary system, requiring additional ETL (Extract, Transform, Load) processes to sync back to your data warehouse.
A Composable CDP avoids these pitfalls by running natively on GCP, treating your existing data stack—not a vendor's platform—as the single source of truth.
## Enter the Composable CDP
A Composable CDP represents the next evolution in customer data management—a modular, cloud-native architecture that replaces rigid, all-in-one solutions with a customizable stack tailored to your business needs. Unlike Packaged CDPs that force you into a vendor's predefined workflows (and their associated costs), a Composable CDP lets you selectively integrate top-tier services already in your GCP environment.
A Composable CDP on GCP leverages services like BigQuery—Google's serverless, petabyte-scale data warehouse—as its foundation. Instead of syncing customer data to an external platform, your CDP runs natively in BigQuery, where you can analyze terabytes of behavioral, transactional, and demographic data in seconds. For example, a retailer could unify online/offline purchase histories in BigQuery, then activate that data in real time via Pub/Sub—all without costly ETL pipelines to a third-party CDP.
The architecture delivers three transformative advantages:
- **True data ownership** – Your data never leaves GCP's encrypted ecosystem, avoiding compliance risks from cross-border transfers.
- **From insights to action** – Native integration with GCP's data streaming services means insights trigger actions instantly (e.g., cart abandonment messages within seconds).
- **Future-proof compliance** – Fine-grained access controls and audit logs are inherited from GCP IAM, simplifying adherence to regulations like GDPR.
By treating your cloud as the CDP—rather than bolting on another SaaS layer—you gain both flexibility and precision where traditional platforms impose constraints.
## Why Composability + GCP = Ideal Architecture
By now, it should be becoming clear that a Composable CDP isn't just an alternative to traditional platforms—it's the natural evolution of customer data management for GCP-centric organizations. The architecture thrives on seamless integration with native GCP services, turning the Google platform's tools into active components of your CDP rather than disconnected silos.
With your CDP built directly on GCP, you can easily tap into Google's most advanced AI capabilities—including Vertex AI for model development and deployment, and Gemini for generative intelligence. Whether you're building predictive audiences, automating personalization, or surfacing real-time insights, these tools are available where your data already lives—no copying, no lag, no closed-loop systems.
This approach delivers unmatched scalability and cost efficiency by leveraging infrastructure you're already paying for, eliminating the redundant storage and processing fees of SaaS CDPs. Critically, it ensures IT teams retain full control—not just over data, but over access policies, privacy safeguards, and compliance workflows. Where packaged CDPs force tradeoffs between functionality and governance, composability on GCP makes them mutually reinforcing.
## The Bottom Line
Aside from vastly improving efficiency and future-proofing business operations, leveraging the power of a Composable CDP on GCP is an almost guaranteed way of boosting revenue. The reason, quite simply, is that by unlocking real-time insights, automating audience targeting, and driving meaningful engagement across multiple marketing and operational functions, it is inevitable that fewer conversion opportunities slip away.
For example, by partnering with Syntasa and implementing a Composable CDP within their GCP environment, one of the UK's leading electronics retailers recently generated a £23M revenue uplift in AI-Driven Product Recommendations and over £30M in additional revenue by improving marketing effectiveness and enabling dynamic retargeting.
These numbers might seem exceptional but they are not unusual; indeed, they are the natural consequence of implementing Syntasa's Composable CDP on GCP.
## FAQ: Composable CDPs on GCP
### Isn't this just building a CDP from scratch?
No—a Composable CDP uses your existing GCP services as building blocks. Think of it as configuring (not coding) with pre-built connectors for tools like BigQuery and Looker.
### How does this work with real-time data?
GCP's Pub/Sub streams events directly to your analytics and activation tools (e.g., sending abandoned cart alerts within seconds), avoiding SaaS CDP processing delays.
### What about compliance?
Your data never leaves GCP, so you inherit its certifications (HIPAA, GDPR). Fine-grained access controls are managed via IAM, not a vendor's opaque permissions system.
## The Only CDP That Truly Belongs on GCP
If you want a CDP that lives natively in Google Cloud—without data leaks, vendor lock-in, or redundant costs—the answer is clear: composable is the only architecture designed for GCP's strengths.
[Ready to see it in action? Explore how Syntasa's Composable CDP leverages your GCP investments to deliver faster insights, ironclad governance, and measurable cost savings—all within your own cloud.](/#brief)
---
# Top 10 CDP Companies and How to Choose the Best One for Your Business
URL: https://syntasa.com/insights/top-10-cdp-companies
Date: 2025-04-22
Category: POV
Audience: Enterprise
With customer data becoming increasingly critical for personalized marketing, choosing the right CDP is essential for businesses looking to unify, analyze, and activate data in real time. The best platforms offer flexibility, seamless integration with existing data infrastructure, and AI-powered insights to drive marketing success. But with so many options available, how do you know which one is right for your business?
In this guide, we break down the top 10 CDP providers, highlighting their unique strengths and how they help businesses overcome common data challenges. Whether you need real-time activation, advanced AI capabilities, or strong data privacy features, this list will help you make an informed decision.
## How to Choose the Best CDP for Your Business
Selecting the right CDP requires a clear understanding of your needs and evaluation of key criteria:
- **Understand Your Business Needs:** Identify your marketing goals, current tech stack, and data infrastructure.
- **Evaluate Key Features:**
- Composability to mix and match best-in-class tools that align with your existing architecture.
- AI/ML Capabilities for deeper insights and personalization.
- Speed of Deployment to achieve quick time-to-value.
- Scalability to support business growth.
- Data Warehouse Integration to centralize and optimize data usage.
- Privacy and Security to ensure compliance and customer trust.
- **Consider Vendor Lock-in:** Opt for a solution that provides flexibility rather than restricting data flow within a proprietary ecosystem.
## Core Capabilities of a CDP: What to Look For in a Solution
When evaluating a CDP, consider these essential features:
- **Composability:** A modular architecture that lets you integrate best-in-class tools for each CDP function, enabling greater flexibility, scalability, and customization based on your tech stack and business needs.
- **Data Ingestion & Integration:** Ability to unify data from multiple sources (CRM, website, mobile, advertising, etc.).
- **Identity Resolution & Customer Unification:** Creating a single, cohesive customer profile.
- **Audience Segmentation & Enrichment:** Real-time segmentation for targeted marketing campaigns.
- **Data Activation & Orchestration:** Seamless integration with marketing, sales, and service platforms.
- **Governance, Privacy & Compliance:** Ensuring adherence to GDPR, CCPA, and other regulatory standards.
- **Analytics, Reporting & Insights:** AI/ML-powered insights, predictive analytics, and campaign performance tracking.
## Top 10 CDP Providers and What They Do Best
Choosing the right CDP is crucial for businesses looking to unify, analyze, and activate customer data in real-time. With so many options available, it's important to understand the strengths of each platform and how they align with your specific needs.
Below, we've highlighted the top 10 CDP providers—both traditional and composable—outlining what sets them apart and how they help businesses overcome data challenges. Whether you prioritize AI-driven insights, seamless data warehouse integration, or real-time activation, this list will help you make an informed decision.
### 1. [Syntasa](/)
An AI-powered Composable CDP that integrates seamlessly with modern data warehouses, enabling businesses to unify, analyze, and activate customer data in real-time while maintaining enterprise-grade security.
Pros
AI-driven features such as propensity modeling and predictive analytics
Seamlessly integrates with existing data warehouses
Unlimited data ingestion and processing
Cons
Best suited for data-forward teams
Not plug-and-play like some traditional CDPs
### 2. [Hightouch](https://hightouch.com)
Known for its strong reverse ETL capabilities, they offer a composable CDP that syncs customer data from warehouses directly into marketing, sales, and support tools, ensuring real-time activation.
Pros
Robust visual audience builder
Built-in campaign analytics native to your data warehouse
Transparent customer support stats (99% satisfaction)
Cons
Deployment can be time-intensive
Requires accessible/actionable data within a platform
### 3. [Segment](https://segment.com)
Allows businesses to collect, unify, and activate customer data efficiently, offering deep integrations with multiple data sources and analytics platforms.
Pros
Well-established event data pipeline
Comprehensive and user-friendly product docs
Single customer view
Cons
Inflexible data model/schema
Data stored outside of your cloud infrastructure
### 4. [RudderStack](https://www.rudderstack.com)
Excels at event streaming and real-time data unification, making it ideal for engineering teams looking for an open-source, warehouse-first approach.
Pros
Open-source flexibility
Uses your existing data platform
Supports ETL pipelines
Cons
Not marketer-friendly
Inflexible deterministic identity resolution
### 5. [Treasure Data](https://www.treasuredata.com)
Strong AI-driven insights and machine learning models, helping enterprises enhance customer personalization and optimize marketing efforts.
Pros
Data import/export capabilities
Built-in tools for data engineering
Supports AI/ML workflows
Cons
Very complex and not marketer-friendly
Identity graph is proprietary
### 6. [mParticle](https://www.mparticle.com)
Specializes in identity resolution and data governance, ensuring brands can build comprehensive customer profiles while maintaining compliance with privacy regulations.
Pros
Works well with mobile apps
Marketer-friendly UI
Strong platform integrations
Cons
Complex setup and long implementation
Data stored outside your infrastructure
### 7. [Zeotap](https://zeotap.com)
Empowers brands to integrate, unify, segment, and orchestrate customer data while emphasizing data protection and compliance.
Pros
Platform-agnostic integration
User-friendly interface
Cons
Potential integration challenges
Higher cost
### 8. [GrowthLoop](https://www.growthloop.com)
A composable CDP that activates targeted campaigns, connects marketing to business outcomes, and accelerates impact with AI-driven insights.
Pros
Customizable architecture
Leverages existing infrastructure
Cons
Requires technical resources
May incur higher processing costs
### 9. [Adobe AEP](https://business.adobe.com/products/experience-platform/adobe-experience-platform.html)
Provides shared, composable, and AI-supported capabilities — including data unification, insight generation, and more.
Pros
Strong Adobe ecosystem integrations
AI-powered segmentation
Identity resolution supports clean rooms
Cons
Requires expensive add-ons
Limited third-party integrations
Long setup timeline
### 10. [Amperity](https://amperity.com)
An AI-powered CDP that specializes in identity resolution, using machine learning to unify fragmented customer data for personalized engagement.
Pros
Advanced identity resolution
Can integrate non-native sources via Fivetran
Deployment assistance via solution architects
Cons
Not user-friendly for marketers
Setup is complex
Pricing tied to MTUs
## Comparison Table of Top CDPs
Below is a feature comparison of the top CDPs:
### What factors should I consider when choosing a CDP?
Key factors to evaluate include AI/ML capabilities, speed of deployment, data warehouse integration, security and compliance measures, scalability, and vendor flexibility to avoid lock-in.
### What industries benefit the most from a CDP?
Industries that require real-time customer data insights—such as retail, e-commerce, finance, healthcare, and travel—benefit the most from CDPs due to their flexibility and AI-driven capabilities.
### How does a CDP improve data activation and marketing performance?
A CDP enables real-time data unification and activation across marketing, sales, and customer service channels, leading to personalized experiences, improved engagement, and higher ROI.
## Conclusion
CDPs are reshaping how businesses manage customer data, offering greater flexibility, faster deployment, and real-time activation capabilities. Selecting the right platform depends on your unique business needs, including AI-driven insights, data security, scalability, and integration capabilities.
If you're looking for a flexible, quick-to-deploy, and AI-powered composable CDP, Syntasa is a top choice. With its real-time data activation, enterprise-grade security, and seamless data warehouse integration, Syntasa empowers businesses to maximize their marketing ROI and enhance customer experiences.
[Ready to transform your customer data strategy? Get in touch with us today to explore how Syntasa can help your business succeed – let's chat!](/#brief)
---
# The Future is Composable: Why CDPs Must Evolve or Die
URL: https://syntasa.com/insights/composable-cdp-must-evolve-or-die
Date: 2025-04-22
Category: POV
Audience: Enterprise
A Wake-Up Call for Customer Data Management
The age of SaaS customer data platforms is over. Businesses today are contending with fragmented customer journeys, strict compliance laws, and the rise of real-time AI personalization–and the tools of yesterday simply aren't built to handle it. On top of that, organizations are looking for more control and transparency. Traditional CDPs often operate as black-box SaaS platforms, limiting access and flexibility.
If your organization is still relying on a traditional, packaged CDP, you may already be falling behind. This isn't just an evolution–it's a full-on shift in how customer data is modeled, activated, and governed.
The future is composable. And if your CDP isn't evolving? It's dying.
## The Problem with Traditional CDPs
Traditional CDPs promised a unified customer view, but in practice, they've delivered rigid, closed-off systems that operate outside your core data stack.
- **Black box architecture:** Limited visibility into data flows.
- **Long implementations:** 6-12 months just to get started.
- **Limited flexibility:** Difficult to adapt to evolving business needs or integrate with existing tech stack.
- **Compliance complexity:** Moving data outside the core stack adds privacy and governance overhead for internal teams.
> "Packaged CDPs were built in a different era. They worked when batch jobs and email blasts were the norm–not when customers expect personalized, real-time experiences."
>
> — [David Chan](https://www.youtube.com/watch?v=sUgwX9JiOhk)
## The Rise of Composable CDPs
Composable CDPs, however, flip the model:
- Modular and interoperable
- Schema-agnostic
- Built on your warehouse (e.g., Snowflake, BigQuery)
- Real-time capable
- AI- and ML-ready
- Unbundled solution
Rather than importing data into a black-box platform, Composable CDPs leverage your existing infrastructure to collect, unify, and activate data in ways that are flexible, efficient, and built for the future.
Want a deeper comparison? Check out [Composable CDP vs Packaged CDP: Which One is Right for Your Business](/insights/composable-cdp-vs-packaged-cdp).
## 5 Reasons Composable CDPs are the Future
### Real-Time Activation
*Supports live streaming, event-forwarding, and sub-second decisioning*
Unlike Packaged CDPs, Composable CDPs provide better real-time activation due to their modular, flexible architecture and ability to integrate seamlessly with modern data ecosystems. Specifically, Composable CDPs leverage streaming data pipelines to process and activate data in real time, whereas Packaged CDPs often rely on batch processing or slower sync cycles. As Composable CDPs decouple storage from activation, they can also enable real-time event-based triggers (e.g., sending an abandoned cart email within seconds) without waiting for a full ETL (Extract, Transform, Load) cycle.
### Compliance by Design
*Data stays within your warehouse, giving you full auditability and control. HIPAA, GDPR, and CCPA are easier to meet.*
With Composable CDPs, organizations can implement specific governance policies tailored to their compliance needs (e.g., GDPR, CCPA) and choose tools that provide enhanced security or data lineage capabilities. For instance, Composable CDPs allow a company to integrate a specialized data governance solution that logs all data access and modifications, providing clear audit trails. With a Packaged CDP, control over governance may be limited by the CDP's built-in features. Users may not have the option to customize governance policies effectively.
### Lower Total Cost of Ownership (TCO)
*With unbundled pricing and no vendor lock-in, you're only paying for what you use.*
Composable CDPs allow organizations to choose only the features they require, avoiding the cost of unnecessary features inherent in packaged solutions. For example, if a business only needs data segmentation and not analytics, they can select that specific module without paying for a full suite. As businesses grow, they can easily add new modules to a Composable CDP. This 'pay-as-you-go' model reduces initial investments and allows for budget-friendly scaling.
### Built for Scale
*No vendor infrastructure limits and no data caps on profiles, sources, or destinations–just the scale of your own cloud environment.*
Composable CDPs are designed to provide flexibility and adaptability in managing customer data across various touch points and systems. Unlike traditional, monolithic CDPs that rely on a single vendor's infrastructure, Composable CDPs leverage a modular architecture that allows businesses to avoid the limitations typically associated with vendor infrastructure. This means there are no arbitrary limits on how much data you can ingest, store, or activate because your data scale is limited only by your own cloud environment. Because Composable CDPs can integrate seamlessly with other platforms and tools in the tech stack, businesses are not compelled by vendor lock-in: components can be switched out without overhauling the entire system, meaning that organizations can take advantage of a spectrum of specialized tools that perform better than what a single vendor may offer.
### Ready for AI and Personalization
*Feed ML predictions and first-party data into customer journeys across every touchpoint.*
Composable CDPs are built as modular systems, allowing organizations to select specific components that best meet their needs. This flexibility enables businesses to integrate specialized AI tools and personalization engines tailored to their unique requirements. For example, if a company identifies a need for advanced machine learning models for predictive analytics, it can easily add that specific functionality without overhauling the entire system.
## But Don't Just Take Our Word For It
Increasingly, industry leaders are encouraging businesses to adopt a composability approach to all their operations as a hedge against unpredictability. As analysts from [Gartner](https://www.gartner.com/en/newsroom/press-releases/2020-10-19-gartner-says-organizations-should-strive-for-composability-to-be-resilient-and-agile-during-uncertainty) have argued, when disruptive change is the norm, it makes a lot of sense to gun for an approach that prioritizes resilience through flexibility. Within the field of technology, Composable CDPs offer exactly that.
Hence, according to the 2024 [Forrester Wave Report](https://www.forrester.com/report/the-forrester-wave-tm-customer-data-platforms-for-b2c-q3-2024/RES181370), 40% of enterprises are considering a Composable CDP approach, with 55% of firms indicating that traditional CDPs fail to meet their flexibility needs. [McKinsey & Company](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights) concur, estimating that by 2026, 50% of large enterprises will replace traditional CDPs with composable data stacks.
## The Risk of Staying Static
The question isn't whether Composable CDPs are the future–it's whether you'll make the shift before it's too late.
Legacy CDPs are already showing cracks. They're too slow, too closed and too outdated to meet the needs of modern digital customers.
The gap is widening. And the longer you wait, the more expensive it gets to catch up.
[Get ahead. Go composable. Schedule a consultation with Syntasa today.](/#brief)
Want to know more about Composable CDPs before taking the next step? Check out: [Composable CDP: A Complete Guide to the Future of Customer Data](/insights/composable-cdp-complete-guide).
---
# Composable CDP vs. Packaged CDP: Which One is Right for Your Business?
URL: https://syntasa.com/insights/composable-cdp-vs-packaged-cdp
Date: 2025-04-14
Category: POV
Audience: Enterprise
Customer Data Platforms (CDPs) have become an essential tool for modern businesses, enabling brands to collect, unify, and activate customer data across multiple channels. However, as the demand for flexible and scalable data architectures grows, companies are now faced with a critical decision: should they adopt a Packaged CDP or build a Composable CDP.
## Introduction
According to [Forrester](https://www.forrester.com/blogs/predictions-2024-data-and-analytics/), over 55% of enterprises are transitioning toward Composable CDPs to overcome the limitations of traditional, vendor-locked solutions. But does that mean it's the right choice for your business?
This guide will break down the differences between Packaged CDPs and Composable CDPs, their pros and cons, and how to determine the best fit for your organization.
For a full breakdown of how Composable CDPs work, read our [comprehensive guide to Composable CDPs](/insights/composable-cdp-complete-guide).
## What is a Packaged CDP?
A Packaged CDP is an all-in-one, vendor-managed SaaS solution designed to provide customer data unification, segmentation, and activation in a single platform. These CDPs require minimal setup and are often favored by businesses that need a turnkey solution.
Benefits of a Packaged CDP:
- **User-friendly interface:** Designed for non-technical users, such as marketers and analysts.
- **Scalable from the start:** No need to worry about building your own data warehouse or data stack if one is not already in place.
- **Comprehensive customer support:** Managed services are typically included.
What's the Downside?
- **Vendor lock-in:** Limited ability to customize data architecture.
- **Scalability issues:** Data needs to be migrated into the CDP, often resulting in duplicate storage.
- **Higher long-term costs:** Pricing often scales with data usage and event volume.
Examples of Packaged CDPs:
- Segment
- Tealium
- BlueConic
> "Traditional CDPs are great for businesses that need an out-of-the-box solution, but they can become a bottleneck when organizations need advanced customization and control over data."
>
> — David Raab, Founder, [CDP Institute](https://www.cdpinstitute.org/)
## What is a Composable CDP?
A Composable CDP is a modular, customizable approach to customer data management. Instead of using a single vendor solution, a Composable CDP leverages best-in-class tools for data storage, transformation, identity resolution, and activation.
According to [Hightouch](https://hightouch.com/blog/cdp-vs-composable-customer-data-platform), in order to be truly composable, a CDP must:
- Provide unbundled pricing
- Be modular and interoperable
- Be schema-agnostic
- Run on your own infrastructure
Benefits of a Composable CDP:
- **Full data ownership:** Control over data governance and compliance.
- **Scalability:** Built to handle enterprise-level data operations.
- **Cost efficiency:** Only pay for the components you need.
- **Real-time streaming:** Supports event forwarding and streaming directly from your warehouse.
- **Data flexibility:** Supports any entity or data model (e.g., households, subscriptions, playlists).
- **Compliance-ready:** GDPR, CCPA, and HIPAA compliant with audit-friendly architecture.
Examples of Composable CDP Components:
- **Data Warehouse:** Snowflake, Google BigQuery, DataBricks
- **Identity Resolution:** Segment, mParticle, Rudderstack
- **Data Activation:** Hightouch, ActionIQ, Twilio Engage
Alternatively, many businesses choose to safeguard the process by partnering with a trusted service provider like Syntasa to help implement the perfectly-tailored Composable CDP package. This allows companies to design, deploy, and scale a Composable CDP that fits both their existing tech stack and their future goals, without the unnecessary complexity of building it themselves.
> "Composable CDPs give companies full control over their data infrastructure, enabling seamless integration with cloud platforms and AI-driven analytics."
>
> — [Gartner](https://www.gartner.com/en/data-analytics)
## Composable CDP vs. Packaged CDP: Feature Comparison
| Feature | Composable CDP | Packaged CDP |
|---|---|---|
| Flexibility | Fully customizable tech stack | Limited to vendor's ecosystem |
| Data Ownership | Full enterprise control | Managed by vendor |
| Scalability | Highly scalable with cloud-native infrastructure | Can be restrictive |
| Cost | Pay-as-you-go, tailored pricing | Subscription-based, scales with usage |
| Data Storage | Stored in your own data warehouse | Stored in vendor infrastructure |
| Event Collection | SDKs load into your warehouse or stream directly | SDKs load data into CDP vendor infrastructure |
| Identity Resolution | Based on any data in your warehouse | Based only on CDP-collected events |
| Audience Management | Supports custom models and entity types | Limited to basic user/account segments |
| Analytics | Core part of architecture | Add-on or separate |
| Compliance | GDPR, CCPA, and HIPAA compliant | Partial GDPR/CCPA; often not HIPAA-ready |
| Data Retention | Unlimited lookback | 1-3 years |
## FAQ
### Which is better: Packaged or Composable CDP?
If your business needs a quick, all-in-one solution, a Packaged CDP is a better fit. If you require scalability, customization, and data ownership, a Composable CDP is the better choice.
### Is a Composable CDP more expensive than a Packaged CDP?
Not necessarily. While a Composable CDP may have higher upfront costs, businesses only pay for the tools they use, making it a cost-effective long-term solution compared to vendor-locked CDPs.
### How long does it take to implement a Composable CDP?
Implementation time varies. Packaged CDPs can usually be set up in weeks, whereas Composable CDPs may require 3-6 months to fully integrate into an enterprise's data ecosystem.
### Can I migrate from a Packaged CDP to a Composable CDP?
Yes, many businesses are transitioning from Packaged to Composable CDPs to gain better control over their data. The migration process involves data mapping, identity resolution, and integrating activation layers with a cloud data warehouse.
## Which CDP Should You Choose?
Both Packaged CDPs and Composable CDPs have their advantages. The best choice depends on your business size, data strategy, and technical resources.
If you need a quick, plug-and-play solution, a Packaged CDP is the right choice. If you want full control over your data stack, a Composable CDP is the future-proof option.
Learn more about the benefits of a Composable CDP in our [Complete Guide to Composable CDPs](/insights/composable-cdp-complete-guide).
[Need expert guidance on choosing the right CDP? Schedule a consultation with Syntasa.](/#brief)
---
# Lookback Windows are Critical for Successful Models with Behavioral Data
URL: https://syntasa.com/insights/lookback-windows-behavioral-data-models
Date: 2025-04-08
Category: POV
Audience: Enterprise
In a constantly shifting digital ecosystem, we need to view data from the perspective of each individual visitor's experience using a relative window.
In a traditional brick-and-mortar store, there are clear operating hours. Most stores are closed for a significant number of hours each day, during which the staff can perform backend tasks such as counting inventory, restocking, and redesigning displays.
However, in today's digital landscape, there is no such downtime. The digital ecosystem is continually evolving, and users are constantly engaging—viewing and clicking on ads, browsing different products, adding items to their shopping carts, navigating away, and making purchases.
The good news is that digital behavior is the most valuable signal in your marketing technology stack. The bad news is that finding this signal within behavioral data poses several challenges, particularly for data engineers who rely on it to build predictive models. Essentially, when engineers sample a dataset, ensuring that each individual has a similar amount of data is non-trivial, as new data is continuously generated and added.
So, how do you build effective models in a constantly evolving ecosystem?
Let's say a data science team within an organization is trying to build a model that predicts whether a visitor is likely to make a purchase. The first instinct might be to simply query all visitor activity within a set period—say, a month—then use that data to train the model and deploy it to production.
This is a common approach among data scientists: sample a period of data, feed it into multiple contender models, select the highest-performing model, and send it to production.
However, this method often results in models that fail to perform as expected in real-world scenarios. The issue? Many models unknowingly incorporate post-purchase data during training, which distorts the predictive capabilities once deployed in production.
For example, imagine a model trained on a dataset that includes visitors who viewed a "thank you for your purchase" page. This page may strongly correlate with purchasing behavior, but it is only seen after a purchase. Including post-purchase data leads to inaccurate predictions because a production model won't have access to this data for users who haven't purchased yet.
## The Solution
At Syntasa, we have spent years developing solutions to challenges like these. Our Sliding Window Framework is designed to address the problem of continuously incoming data and ensure predictive models are trained effectively.
Rather than using a generalized time frame for all visitors, our approach considers each visitor's journey individually. The key issue is that visitors can make a purchase at any point—Day 3, Day 15, Day 23, etc. A standard fixed-time dataset may contain post-purchase activity, which skews model training.
Syntasa's framework resolves this by filtering behavioral data to include only pre-purchase actions within a specified lookback window. This approach ensures that models are trained on a dataset that accurately reflects real-world user behavior and excludes data that won't be available in production.
A sliding window framework more accurately approximates production environments. As we've established, in the real world, your model will only have access to data generated before the purchase. Therefore, training your model using a sliding window approach ensures that its results do not rely on data that won't be available in a production environment.
This more personalized approach also allows for a uniform time period when building user histories. Instead of the length of a user's history varying based on when their purchase occurred within your timeframe, every user history will contain the same number of days of activity. For example, rather than having 17 days of activity for a user who purchased on day 17 and 26 days for a user who purchased on day 26, both would have the same number of days recorded. The optimal number of days required for an accurate model will vary by use case.
By applying this approach, a data science team building a propensity-to-purchase model can ensure they're training on the right data—behavioral signals that are truly predictive rather than misleading. Syntasa's Sliding Window Framework filters out post-purchase activity, aligns user histories to a consistent pre-purchase window, and more closely mirrors the conditions under which the model will operate in production.
The result? Models that are more reliable, more accurate, and better aligned with real-world user behavior—so you can predict who's most likely to buy before they do, not after.
## FAQs
### Why does a lookback window matter in AI modeling?
The lookback window determines how much historical data an AI model considers, shaping its ability to recognize trends and make accurate predictions. Selecting the right timeframe ensures the model is neither too short-sighted nor bogged down by outdated patterns.
### What challenges arise from using the wrong lookback window?
A short lookback window may miss critical long-term behavioral patterns, leading to underfitting. A long window, on the other hand, can introduce outdated trends, causing overfitting and inaccurate predictions.
### Can AI models automatically optimize their lookback windows?
Yes, advanced AI models can analyze real-time data and adjust their lookback windows automatically. This ensures that predictions remain relevant without requiring constant manual fine-tuning.
## Final Thoughts
Beyond ensuring clean training data, selecting the best model from a set of contenders is equally critical. Syntasa's expertise in behavioral data processing and predictive modeling allows businesses to maximize the value of their digital signals. Our Sliding Window Framework provides a robust foundation for building predictive models that remain effective in a dynamic digital environment. By ensuring that models are trained on clean, structured, and real-world-relevant data, we help organizations make data-driven decisions with confidence.
[Ready to optimize your AI models and want expert guidance? Contact us to see how we can help optimize your predictive modeling strategy.](/#brief)
---
# Composable CDP: A Complete Guide to the Future of Customer Data
URL: https://syntasa.com/insights/composable-cdp-complete-guide
Date: 2025-04-02
Category: POV
Audience: Enterprise
The concept of Customer Data Platforms (CDPs) emerged in the mid-2010s, spurred by the need for businesses to unify customer data across various touchpoints and channels. Since then, CDPs have revolutionized the capacity of businesses to leverage vast swathes of customer data in the pursuit of effective, ultra-targeted marketing campaigns.
## What is a Composable CDP?
*The Next Evolution in Customer Data Management*
The landscape of customer data management, however, is ever changing, and businesses are demanding more flexibility, control, and scalability than ever before. Traditional packaged CDPs are looking increasingly cumbersome, often coming with significant limitations–rigid architectures, vendor lock-in, and high costs–that can restrict an organization's ability to harness the full potential of its customer data. Enter, Composable CDPs.
A Composable CDP is a modular, flexible approach to customer data management that allows businesses to integrate best-in-class tools for data storage, transformation, and activation. Unlike traditional CDPs, which provide an all-in-one solution, composable CDPs give organizations complete control over their tech stack, ensuring scalability, security, and adaptability.
## Why are Businesses Adopting Composable CDPs?
*Traditional (Packaged) vs. Composable CDPs*
Businesses today require agile, customizable data solutions that align with their evolving digital ecosystems. With increasing pressure to deliver real-time, hyper-personalized customer experiences, companies need data platforms that seamlessly integrate with their existing cloud infrastructure, analytics tools, and marketing platforms.
Some of the key differences between packaged and Composable CDPs are highlighted in the table below:
| | Packaged CDP | Composable CDP |
|---|---|---|
| Flexibility | Limited to built-in features | Fully customizable tech stack |
| Scalability | Costs can rise with high profile volumes | Scales with cloud-native infrastructure |
| Data Control | Vendor-managed (black box) | Full enterprise control |
| Integration | Difficult to bring data back into data warehouse for modeling | Open ecosystem that connects with modern data platforms |
| Time to Value | Packaged CDPs typically require a 12+ months implementation time to see results | Composable CDPs can be set up in 12 weeks |
> "Composable CDPs offer a groundbreaking approach to customer data management, emphasizing flexibility, scalability, and customization over the rigid structures of traditional platforms."
>
> — Acceldata.io
## How Composable CDPs Work
Understanding how a Composable CDP functions is key to leveraging its flexibility and scalability. Unlike monolithic, all-in-one CDPs, which come pre-built with specific features, a Composable CDP architecture consists of separate, best-in-class components that work together to create a highly efficient, scalable data infrastructure.
A Composable CDP architecture consists of the following key components:
- **Data Storage Layer:** Cloud-based data warehouses such as Snowflake, Google BigQuery, or DataBricks.
- **Data Processing Layer:** ETL/ELT tools like dbt, Fivetran, and Apache Spark for data transformation.
- **Identity Resolution Layer:** Unifying customer data using identity graphs (e.g., Segment, mParticle).
- **Activation Layer:** Connecting customer profiles to marketing, sales, and analytics platforms (e.g., Google Ads, Salesforce, Braze).
## Key Benefits of a Composable CDP
Today's businesses need agile, data-driven solutions to keep up with shifting customer expectations. A Composable CDP provides significant advantages, including:
- **Better Data Ownership & Security:** Unlike packaged CDPs, where third-party vendors control customer data, Composable CDPs ensure full data autonomy and compliance with GDPR and CCPA.
- **Faster Insights & Activation:** Because data flows directly from the warehouse to activation tools, businesses can react in real-time rather than relying on batch processing delays.
- **Cost Efficiency:** Organizations can build and pay only for the tools they actually need, rather than purchasing an expensive full-suite CDP that includes unnecessary features.
- **Scalability:** Designed to support enterprise-level data operations, a Composable CDP ensures high performance even with millions of customer interactions.
## Real-World Use Case of Composable CDPs
Composable CDPs may be relatively new, but there can be no doubt about their use-value. World-renowned brands from every industry have already made the switch and are seeing huge increases in their lead conversions, not to mention significant bumps in their revenue.
### From Rule-Based to Results: $7M in Revenue, $5M in Savings
A global electronic retailer suspected its online marketing strategy wasn't living up to its full potential. The company had relied on rule-based logic for display retargeting, but the results were inconsistent. Customer segments lacked precision, leading to unpredictable funnel journeys—and too many leads slipping through the cracks. By shifting to a Composable CDP that analyzed 100 variables and coefficients, the electronic retailer was able to zero in on its most valuable audience segments. The result? $7M in incremental revenue generated within 30 days, plus $5M in annual savings on display advertising. Building on that success, the company is now scaling its custom customer data stack across all global marketing channels—including email, SEM, and affiliates—and has earmarked $20M in projected savings for reinvestment into marketing.
### £23M Boost in 2024—Plus £45M from Abandoned Carts
A leading electronics retailer in the UK and Ireland, faced a major challenge: the lag between manually processing customer insights and putting them into action. This delay often meant missed opportunities for real-time personalization and accurate product recommendations, leading to cart abandonment and lost sales.
By implementing a Composable CDP within their Google Cloud Platform, they streamlined three critical areas: Marketing Activation, Data Optimization, and AI/ML Modeling. The results were transformative: in 2024 alone, they achieved a £23M revenue uplift, while the CRM team generated over £45M through cart abandonment campaigns.
### Optimized Ad Spend Drives 161% Increase in Revenue
A [leading online marketplace](https://www.simondata.com/case-studies/better-social-media-segmentation-via-engagement-data) for custom merchandise struggled with unclear return on ad spend (ROAS) while relying on Facebook's Pixel platform. The lack of visibility made it difficult to confidently scale paid advertising. After switching to a Composable CDP, the team eliminated those blind spots—gaining a clearer view into performance and audience behavior. With better data, they optimized ad spend, resulting in a 161% increase in paid social revenue and a 21% lift in ROAS.
### Unifying Data to Drive $350K in Incremental Revenue
A [leading online travel and leisure company](https://www.simondata.com/case-studies/how-travel-leisure-co-knocked-down-data-silos-and-achieved-1-1-personalization) realized that fragmented customer data—scattered across marketing platforms, call centers, and websites—was limiting performance. To address this, they adopted a Composable CDP tailored to their specific marketing strategy. By eliminating redundant tools and streamlining their stack, they doubled productivity, reduced tech spend by 30%, and unlocked an additional $350K in annual incremental revenue.
These stories show what's possible—but how do you get started? Building a Composable CDP might sound complex, but the process can be straightforward with the right approach.
## How to Build a Composable CDP
### Step 1: Define Business Requirements
Before implementing a Composable CDP, organizations must identify their key data needs and business objectives.
- What data sources will be integrated (CRM, analytics, marketing platforms)?
- Is real-time data processing essential for business operations?
- How will customer identities be resolved across multiple channels?
### Step 2: Choose the Right Data Stack
Selecting the right tools is crucial for an efficient and scalable Composable CDP.
- **Cloud Data Warehouse:** Snowflake, BigQuery, Databricks
- **ETL/ELT Tools:** dbt, Fivetran, Apache Spark
- **Identity Resolution:** Segment, RudderStack, mParticle
### Step 3: Activate & Scale
Once the data infrastructure is in place, businesses can:
- Connect marketing automation tools (Braze, Salesforce, HubSpot) to drive real-time customer engagement.
- Implement real-time segmentation to deliver hyper-personalized marketing campaigns.
- Continuously optimize the system to ensure scalability and performance.
Syntasa operates directly within your cloud environment, creating a customized data foundation that powers advanced analytics, modeling, and activation—all without moving your data.
With partners like Syntasa, companies can design, deploy, and scale a Composable CDP that seamlessly integrates with their existing tech stack and business objectives—eliminating unnecessary complexity.
## The Future of Composable CDPs
As customer data management evolves, Composable CDPs are expected to become the dominant architecture for enterprises seeking flexibility, scalability, and real-time, AI-powered decision-making. Here are some of the key trends shaping the future of Composable CDPs:
- **AI-Driven Automation Will Replace Manual Data Processing:** Predictive analytics, autonomous segmentation, and real-time customer insights will become standard features.
- **Real-Time Data Streaming Will Dominate:** Batch processing will disappear, replaced by instant data activation across platforms.
- **Composable CDPs Will Become the Industry Standard:** Enterprises will fully transition away from packaged CDPs by 2027. (Source: [Gartner](https://www.gartner.com/en/articles/gartner-s-top-strategic-predictions-for-2024-and-beyond))
- **Privacy-First Data Strategies Will Become Mandatory:** Companies will need to comply with stricter data privacy regulations while still enabling personalization. (Source: [McKinsey](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai))
- **Cloud Data Warehouses Will Replace Packaged CDPs:** Businesses will custom-build their own data ecosystems instead of relying on vendor-managed CDPs.
> "The future of martech lies in composability, where brands own their data and use the tools they need, without being forced into rigid, monolithic platforms."
>
> — Charmee Patel, Head of Data and AI at Syntasa
## FAQs
### What's the difference between a Composable CDP and a Packaged CDP?
A Composable CDP allows modular integration with best-in-class tools, while a Packaged CDP is an all-in-one solution with pre-built features.
### Is a Composable CDP more expensive?
Not necessarily–costs are based on usage and required tools, which can be more cost-efficient than bundled software feeds.
### How long does it take to implement a Composable CDP?
Most companies see full deployment within 3-6 months, depending on integration complexity.
## Conclusion: Why Composable CDPs are the Future
A Composable CDP empowers businesses with greater flexibility, scalability, and control over customer data. As AI-driven search and real-time customer engagement grow, companies that embrace a composable approach will lead the market.
[Ready to build your Composable CDP and future-proof your customer data strategy? Schedule a consultation with Syntasa.](/#brief)
---
# Syntasa Data + AI Platform Joins Google Cloud Ready — Distributed Cloud
URL: https://syntasa.com/insights/syntasa-google-distributed-cloud
Date: 2024-04-09
Category: NEWS
Audience: Mission
We are thrilled to announce a significant milestone: Syntasa's Data + AI Platform is one of a select few portfolio of software applications available on Google Distributed Cloud. This marks a pivotal moment in our journey – enabling our no-code, low-code, and pro-code application to orchestrate cloud-native services in edge and air-gapped deployments while leveraging the latest advances in data analytics, Generative AI, and machine learning.
This partnership enhances our offerings within [Google Cloud Marketplace](https://console.cloud.google.com/marketplace/product/syntasa-public/syntasa-behavioral-sentiment-analytics?project=syntasa-saas) and allows us to offer additional data and artificial intelligence solutions to companies that need distributed and on-premise cloud operations.
## Creating Value For Customers:-
Google Distributed Cloud brings the benefits of public cloud computing beyond hyperscale regions. It addresses several challenges that have arisen from very high local processing, regulation, and noisy or limited connectivity.
> "Google Distributed Cloud provides new capabilities that are relevant for our customers in several industries, including retail, financial services, and public sector. Syntasa's advanced Data + AI Platform gives Google Distributed Cloud customers the power to create AI solutions significantly faster in edge and air-gapped environments and Google Cloud's leading data analytics and artificial intelligence technologies provide an immediate step up in capabilities and allows us to dream bigger."
>
> — Jay Marwaha, CEO and Founder of Syntasa
Retail: With a flexible approach to data analytics, our platform empowers retailers with many locations to strategically place intelligence closer to the customer and enable informed decision-making in a decentralized manner.
Financial Services: In the complex landscape of financial regulations, our platform enables secure data storage and analysis while ensuring compliance with industry regulations, such as data residency and sovereignty.
Public Sector: Trusted by the public sector, our Data + AI application has been accredited in the most highly secure environments. Google Distributed Cloud allows data engineers and data scientists to use our platform's advanced technology to analyze sensitive datasets in air-gapped environments, meeting the unique needs of these government agencies.
> "Our collaboration with Google Distributed Cloud marks a significant milestone, enhancing accessibility to our Data + AI Platform. We're excited to extend our capabilities to new deployments, offering repeatable, reliable, and easily maintainable Data + AI pipelines. By incorporating cutting-edge Generative AI, including Vertex AI platform, we empower technical and non-technical users to leverage advanced analytics. This collaboration opens doors for businesses across sectors to harness the power of data and AI in a user-friendly manner, fostering innovation and growth."
>
> — Shawn Zargham, CTO and Cofounder, Syntasa
## About Syntasa
Syntasa are digital behavior experts. Our no-code, low-code, pro-code application orchestrates cloud-native services and leverages the latest advances in AI and machine learning to gain insights from digital behavior and activate those insights autonomously.
We deliver solutions for our clients in the commercial, public sector, and national security sectors. In commercial, we focus primarily on marketing solutions, including CDP, audiences, and personalized recommendations. In the public sector, we analyze digital behavior and conversation within the community to get insights into needs and attitudes, as well as improve the experience of digital services. Our national security clients use us as an enterprise Data and AI/ML platform to deliver many use cases and apps.
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# Celebrating the Winners of the 2021 Google Cloud Customer Awards
URL: https://syntasa.com/insights/google-cloud-customer-awards-2021
Date: 2022-06-14
Category: MENTIONS
Audience: Mission
Google Cloud recognized the Oklahoma Department of Mental Health and Substance Abuse Services (ODMHSAS), a Syntasa customer, as a winner of its 2021 Customer Awards for its work with Syntasa.
Originally published on the Google Cloud blog.
Source: https://cloud.google.com/blog/products/gcp/celebrating-the-winners-of-google-cloud-customer-awards
---
# Google Cloud: How the State of Oklahoma Is Using Data to Fight the Opioid Epidemic
URL: https://syntasa.com/insights/oklahoma-opioid-epidemic-data-ai
Date: 2022-02-15
Category: MENTIONS
Audience: Mission
Learn how the State of Oklahoma is leveraging data and AI to address one of the nation's most urgent public health crises. This article from Google Cloud highlights how the state integrates siloed systems, accelerates access to critical insights, and empowers frontline workers with real-time data to support prevention, treatment, and recovery efforts.
Originally published on the Google Cloud blog.
Source: https://cloud.google.com/blog/topics/public-sector/how-state-oklahoma-using-data-fight-opioid-epidemic
---
# Reevaluating Vaccine Sentiment: An Episode of the AI: Gov Reimagined Podcast
URL: https://syntasa.com/insights/reevaluating-vaccine-sentiment-podcast
Date: 2021-12-16
Category: MENTIONS
Audience: Mission
An episode of the AI: Gov Reimagined podcast, presented by Google Cloud, exploring how the State of California used sentiment analytics — built in partnership with Syntasa — to understand vaccine hesitancy and redistribute public health resources more effectively.
Originally published on Government Executive.
Source: https://www.govexec.com/sponsors/ai-gov-reimagined/2021/12/reevaluating-vaccine-sentiment/187360/
---
# Data Con LA 2021: Scaling Sentiment to Geotarget Vaccine Distribution
URL: https://syntasa.com/insights/data-con-la-2021-vaccine-sentiment
Date: 2021-11-03
Category: MENTIONS
Audience: Mission
A joint Data Con LA 2021 session with California's Office of Digital Innovation and Syntasa's CTO & Co-Founder, Shawn Zargham, on how digital surveys and statistical modeling estimated COVID vaccine hesitancy at the zip-code level — data that directly informed vaccine allocation strategy.
Originally presented at Data Con LA 2021.
Source: https://www.youtube.com/watch?v=1FI3G_uIi3Y
---
# AHCCCS Launches New Opioid Services Locator
URL: https://syntasa.com/insights/ahcccs-opioid-services-locator
Date: 2021-10-12
Category: MENTIONS
Audience: Mission
Arizona's AHCCCS (a Syntasa customer) launched a new web-based Opioid Services Locator — a real-time search tool connecting Arizonans to certified opioid treatment programs, residential services, and Naloxone access by location and health plan network.
Originally published by the Arizona Health Care Cost Containment System (AHCCCS).
Source: https://www.azahcccs.gov/shared/News/PressRelease/OpioidServiceLocatorLaunch.html
---
# Google Cloud: Getting Vaccines Into Local Communities Safely and Effectively
URL: https://syntasa.com/insights/vaccines-local-communities-google-cloud
Date: 2021-02-01
Category: MENTIONS
Audience: Mission
Google Cloud's Intelligent Vaccine Impact solution — with a Sentiment Analysis component built in partnership with Syntasa — helped regional and local governments understand constituent sentiment and deliver more effective COVID-19 vaccination strategies.
Originally published on the Google Cloud blog.
Source: https://cloud.google.com/blog/topics/public-sector/getting-vaccines-local-communities-safely-and-effectively
---
# Infoworld: 8 Misleading AI Myths — and the Realities Behind Them
URL: https://syntasa.com/insights/infoworld-ai-myths
Date: 2020-01-27
Category: MENTIONS
Audience: Mission, Enterprise
Syntasa CEO Jay Marwaha is quoted in this InfoWorld feature separating fact from hype across eight common AI myths — and why having no AI plan is itself a competitive risk.
Originally published on InfoWorld.
Source: https://www.infoworld.com/article/3514577/8-misleading-ai-myths-and-the-realities-behind-them.html
---
# Syntasa Named a Gartner Cool Vendor in Personalization
URL: https://syntasa.com/insights/syntasa-named-gartner-cool-vendor-personalization
Date: 2018-06-15
Category: NEWS
Audience: Enterprise
Gartner has praised Syntasa's capabilities to combine clickstream data with enterprise data to deliver a more holistic view of customer behavior, from event-triggered marketing and tailored messaging to optimized customer experiences based on interactions across touchpoints.
Syntasa was recently named by Gartner as a Cool Vendor in Personalization. Gartner has praised Syntasa's capabilities to combine clickstream data with enterprise data to deliver a more holistic view of customer behavior, from event-triggered marketing and tailored messaging to optimized customer experiences based on interactions across touchpoints.
The report, produced by analysts [Jennifer Polk](https://www.gartner.com/analyst/46806/Jennifer-Polk), [Bryan Yeager](https://www.gartner.com/analyst/62940/Bryan-Yeager), and [Augie Ray](https://www.gartner.com/analyst/57069/Augie-Ray), describes how personalization is a vital marketing technology, enabling stronger customer relationships and a competitive edge through customized content.
Large companies including Lenovo, Sky, Tesco, Telegraph Media Group, and others are using [Syntasa's Customer Intelligence Platform](/platform) to unify disparate sources of rich behavioral stream data and transform them into intelligent experiences. From leveraging anomaly and fraud detection across advertising activity, to producing omni-channel journey models which trigger retargeting and next-best action campaigns.
This report can assist marketing leaders in filtering out the hype surrounding AI-powered marketing platforms, and to identify those platforms which are truly delivering tangible results. With Syntasa this means redefining the personalization engine category and recognizing the big missing capabilities:
- Leverage existing internal data pipelines and sources without moving data externally
- Automated data preparation transforming data into intelligence
- Agnostic next-best action decisioning to inform relevant personalization anywhere
- Automated learning and scoring rooting decisions in improved KPIs
- Configurable data science model builder and bring your own algorithms for data scientists to centralize, deploy and test models at scale
With Syntasa these also sit natively inside your existing architecture (critical for GDPR compliance) so no additional marketing cloud needs to bolt on. Marketing teams with mature data-driven capabilities and analytics functions, as well as access to enterprise data, should consider Syntasa to help scale their teams' ability to leverage big data for advanced modeling, which in turn drives optimal personalization.
To view the full report, [download the PDF](/insights/syntasa-gartner-cool-vendor-report.pdf).