Deployable knowledge graph that connects intelligence across classification boundaries. Full source traceability. Zero external dependencies.
Intelligence products are created in isolation across units, classification levels, and time periods. Analysts working on related threat streams often produce redundant or incomplete assessments because no system maps the relationships between their work. The connections surface during briefings — or they don't.
An analyst in one unit produces an assessment on a threat network. Two weeks earlier, another unit completed a related product covering overlapping entities. Neither analyst sees the other's work. Across large organizations, this isn't an edge case — it's the default. Redundant analysis wastes hundreds of analyst hours per year, and worse, critical connections between threat streams go undetected.
Classification boundaries compound the problem. The most important context often lives on the other side of a classification boundary. Analysts carry insights between systems manually, losing fidelity and attribution at every handoff. The result is assessments built on incomplete foundations that nobody can trace end-to-end.
And when a senior analyst rotates or retires, the operational understanding they built over a decade — which entities are connected, which sources are reliable, which historical patterns matter — leaves with them. Onboarding their replacement takes months. Some of that knowledge is never recovered.
Anatypical builds a cross-product knowledge graph that maps entity relationships across finished intelligence, raw reporting, and analyst notes. It deploys on-premises with zero external dependencies, supports any LLM, and provides full source traceability via Glass Box — a transparency layer that shows exactly where each answer came from and flags unsupported claims.
When an analyst queries the system, they don't just get relevant documents — they get the relationships between entities, the chain of reporting that connects them, and the previous analytic products that touched the same space. Redundant analysis becomes visible before it happens.
Every answer includes a complete source trail — which documents contributed to the response, how they were retrieved, and a trust score reflecting confidence in the answer. Unsupported claims are flagged automatically. Reviewers can verify sourcing without re-reading every cited document.
Anatypical deploys on-premises or into your cloud environment. No data transits external networks. The platform is model-agnostic — bring your own LLM or use any model that meets your security requirements. Retrieval parameters, ontology definitions, and access controls are fully configurable.
The knowledge graph doesn't reset between sessions. Every document ingested and every analyst interaction compounds the graph's understanding. When an analyst departs, their contributions remain — entity mappings, connection patterns, annotated relationships. The replacement inherits a living knowledge base, not an empty inbox.
A junior analyst takes over coverage of a threat network after a senior colleague's departure. They query Anatypical: "What are the key entities and relationships in this network?"
The system returns a structured overview: primary entities, their relationships, the reporting that established those connections, and links to every previous analytic product that covered the network. The analyst sees not just what's known — but the provenance chain for how it came to be known.
Within a week, the new analyst is producing assessments grounded in the full institutional history. Without Anatypical, this ramp-up takes three to six months.
Most intelligence environments rely on keyword search across document repositories. This works when an analyst knows what they're looking for — a specific report number, a known entity name. It fails when the question is relational: 'What connects this entity to this funding stream?' or 'Has anyone assessed this pattern before?' Keyword search returns documents. Anatypical returns relationships, provenance, and structured knowledge.
