Make every image AI-ready — without it ever leaving your boundary.

Brings imagery, FMV, sensor, geospatial, text, and event data out of legacy repositories and into governed, AI-ready datasets at petabyte scale, resolving the same object or entity across sources into one record. All-source fusion at every classification level.

Lineage, classification handling, and policy are enforced from the moment data arrives; row-, column-, and classification-level policy is enforced every time it's read.

Syntasa deploys as a Kubernetes application inside your own environment, not a custom build. A U.S. federal agency went from air gap to operational use in six weeks.

Because data never leaves your boundary, you inherit your existing authorizations instead of starting over.

Imagery and full-motion video processed continuously inside the boundary, so AI keeps pace with collection.

At a U.S. federal agency, mission streaming computer-vision data — high-definition imagery — is processed continuously, entirely air-gapped, on customer-owned on-prem infrastructure.

Open-source and partner object- and change-detection models are incorporated into the Syntasa platform, running on your imagery inside your boundary. Every model runs under the same model custody, governance, and audit trail as everything else — with no separate analytics tool to buy or accredit.

Every request, human or agent, is bound to an identity in your own identity provider. Every read, transformation, and export is logged to an append-only audit trail you export to your SIEM.

Agents that triage, enrich, and route imagery act only within a declared, reviewable authority — never more than the person who invoked them — with time-boxed credentials, no route out, and the full causal chain behind every decision.

Models trained and served only inside your boundary, with registry, lineage, and drift monitoring.

Predictable enterprise licensing instead of consumption-based charges that grow with every terabyte, and up to 50% lower cost to run workloads through intelligent autoscaling and auto-termination.

Built on pure open source (Spark, Delta Lake, Iceberg, Ray, MLflow), so you own your data, workflows, and migration path. AI-assisted migration from managed data and AI platforms included.

Low/no-code Syntasa Apps let analysts build full pipelines visually, compiling to the same optimized Spark engineers write by hand; notebooks in Python, SQL, Scala, and R; a built-in AI Code Assistant. Fewer cleared engineers needed per workload.