
Connect every system. Reason at the edge. Decide without reach-back.
Mission Data Fabric unifies every source, sensor, and mission system into one governed semantic layer, then runs agentic AI that reasons at the tactical edge. Scattered data becomes all-source, decision-ready intelligence with full provenance, online or fully disconnected, on the infrastructure you already run.
The Problem
Mission data is trapped in disconnected systems that each speak their own language, and the operator forward often loses reach-back the moment comms degrade. Sensors, all-source feeds, mission systems, and maps each have data, but nothing has shared meaning, so nothing connects and nothing is explainable. Stitching it together by hand is slow, and it breaks exactly when it matters most.
A connected progression, not separate tools.
One shared foundation.
A doctrine-aligned model of the mission domain: the entities, the relationships between them, and the rules that govern them. The foundation every system builds on.
The ontology, alive.
That ontology as a living knowledge graph, queryable across every discipline.
No copy. No migration.
The graph acts inside the systems where work already happens, so workflows and AI use it without copying the data.
Capabilities
Six capabilities that turn scattered data into decision-ready intelligence.
One doctrine-aligned ontology unifies every source, sensor, and system into shared meaning.
Constraint-aware AI agents correlate all-source, prioritize, and explain. Grounded and cited.
Knowledge graph and geospatial map give commanders a single view of who, what, where, and why.
Every entity traces to its source. NIST AI RMF lineage, IC analytic standards, RMF/ATO-ready.
Full autonomy in DDIL with zero reach-back. The graph and agents sync when comms return.
You keep the data, models, and knowledge graph the platform builds. No lock-in.
How It Works
Sensors and open-source feeds enter a gateway, normalized to one observation envelope.
Observations land in the edge database and fuse into one living knowledge graph.
Local inference correlates multi-source data into one assessed picture. Every answer is grounded and cited.
Workflows read the graph in place: common operating picture, decision queue, and co-pilot.
Edge-resident and disconnected-ready. The same flow runs forward in DDIL and syncs back when comms return.
Analysts work the graph. Commanders work the map. When they're built on the same entities, the two stop disagreeing: select anything in one view and it's the same object in the other.
Ask who, what, and why. Trace any entity to its evidence, its confidence, and where it came from.
Who, what, and why
SAME
ENTITY
See where it is and what's around it. The same entities from the graph, placed on the map for the commander's decision.
Where
Edge-Ready
The full fabric (agents, ontologies, graph inference) runs on a ruggedized edge node, deployable in a fly-away kit.
Full autonomy in comms-denied (DDIL) environments.
Confidence, source authority, and time carried on every assertion, aligned to IC analytic standards.
Bring the analysis to the data. No ETL, no migration, governed and traceable from enterprise to edge.
The ontology and knowledge graph are the through line. Every Mission Data Fabric output traces back to them.
Request a mission demo, or give us your toughest disconnected-operations problem. Let's scope a pilot on your terms.
Prefer to talk? Schedule a 30-minute session with our public sector team.
Book NowChris Walker, VP of Federal Markets
Caitlyn McClellan, SVP of Federal Sales