How a matter graph connects people, entities, positions, filings, contracts, and decisions so legal AI can answer with context.
For legal teams evaluating knowledge graphs and private AI for complex, privileged, or regulated matters.
Primary search phrase: AI knowledge graph for legal matters. Related searches include legal knowledge graph, AI legal matter graph, private legal AI.
A legal matter is not only a folder of documents. It is a network of parties, counsel, regulators, contracts, claims, positions, deadlines, filings, and decisions. AI that ignores those relationships will miss context.
A matter graph helps the system assemble better context before answering. It also gives the team a visible map of what the workspace believes is connected.
A graph should not become an unsupported assertion layer. Legal teams need to see which source records support a relationship and which documents support an answer.
Steward's approach is to keep graph, vault, and cited answers together. The user should be able to move from answer to source, and from source to related people, entities, and decisions.
Ask questions that require relationships: which positions has a counterparty taken, which documents support a claim, which obligations remain open, or where prior advice changed.
The demo should show whether the graph adds practical value for legal review, not merely whether it looks interesting on screen.
Because legal questions often depend on relationships: parties, positions, obligations, prior advice, filings, and evidence.
It should be. Sensitive legal workflows need inspectable links and source references rather than hidden derived context.
No. The same pattern applies to regulatory matters, investigations, transactions, contract portfolios, and governance work.
The live demo uses fictional records, so your team can inspect Steward's graph, vault, House Rules, citations, and team memory before sharing sensitive material or starting procurement.