Multi-intelligence fusion is the discipline of turning heterogeneous sensor and reporting feeds — SIGINT, GEOINT, OSINT, full-motion video, structured operational data — into something an analyst or commander can actually query. Zapata AI has been building this pipeline since 2018.
Why fusion is hard
Each INT is collected, classified, and retained on its own schedule and under its own rules. Joining them without losing provenance, without crossing classification boundaries, and without creating a synthesis that outruns the underlying evidence is a nontrivial engineering problem. Most “AI fusion” demos skip this entirely; ours doesn’t.
Our approach
- Schema-preserving ingestion: every record keeps its source, classification, collection context, and confidence.
- Entity resolution across sources — a call sign, a phone, a vehicle, a face — tracked and reconciled as evidence accumulates.
- Natural-language query backed by retrieval that cites its sources, so the analyst sees not just an answer but the feeds it came from.
- Level-appropriate redaction before sharing downstream, derived from the classification markings the data carried in.
Downstream in ANIMAS
Multi-int fusion is the analytical core of our ANIMAS platform. ANIMAS uses generative AI to let an operator “talk to the data” — asking questions in natural language and receiving answers tied back to the underlying feeds. See the ANIMAS product page for architecture details.