Turn documents into a queryable map of facts and relationships.

Graph extraction is only the beginning. Enterprise users need to know which document supports an assertion, when it was valid, when the system learned it, whether a reviewer approved it, and what happened when sources disagreed.

We treat extracted facts as candidates that pass through schema validation, grounding, confidence checks, and review before materialization. The graph becomes a navigable evidence layer rather than an untraceable collection of AI guesses.

• Ontology and schema design for the questions the business needs to answer
• Document-to-graph extraction with passage-level evidence
• Entity resolution that merges duplicates reversibly and auditably
• Temporal modeling for what was true and when the organization knew it
• Conflict detection when sources disagree on dates, amounts, identities, or obligations
• Human review queues for low-confidence, sensitive, or contradictory assertions

For federal, defense, and intelligence programs, graph design can align to Basic Formal Ontology and Common Core Ontologies. Versioned schema packs define classes, relations, identity rules, and validation so conformance can be reviewed as an artifact rather than asserted in a slide.

For commercial use cases, the same discipline supports contract intelligence, obligation tracking, regulatory analysis, customer and vendor views, and research over filings or internal reports.

We start with a bounded set of questions, source documents, entity types, and review roles. A pilot proves extraction quality, identity rules, evidence navigation, and conflict behavior before the ontology expands. Search, agents, and applications can then consume the approved graph through the same governed platform.