Industry: Legal

Knowledge graphs for legal and law firms.

Legal knowledge graphs and GraphRAG that eliminate fabricated case citations, cut contract review time by 60 to 80%, and give every research memo paragraph level source references. For firms, in house counsel, and legaltech vendors: contract clause graphs, case law citation networks, transitive conflict checking, and e discovery entity resolution.

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Why legal AI keeps failing on citations: and how graphs fix it

The Fabricated Citation Failure Mode

The most publicized legal AI failures (attorneys sanctioned for briefs citing fabricated cases) all share one cause: the LLM was generating from parametric memory instead of retrieving from a verified source. Vector RAG helped, but similarity is not the same as authority; a plausible sounding neighbouring case is still the wrong case.

Why Legal Work Is Graph Shaped

Legal work is inherently graph shaped. Cases cite cases. Contracts reference other contracts. Corporate families own subsidiaries. Custodians talk to custodians. Modelling those relationships as a graph gives the LLM a substrate that mirrors how lawyers actually reason about a matter.

Grounding Makes Fabricated Citations Impossible

Grounding the LLM in the graph plus your matter documents makes fabricated citations effectively impossible, because the model has to point at a real node. The assistant either cites a real case that exists in Westlaw or Lexis, or it refuses. There is no third option.

Six proven legal use cases

Contract clause graphs and playbooks

Every executed contract becomes a graph of clauses, obligations, parties, and effective dates. Compare against your playbook, surface deviations in seconds, and answer 'which contracts have MFN clauses expiring in Q3'.

•Clause level search across the entire executed corpus, not just filenames
•Automated deviation reports against the current playbook version
•Renewal, audit, and change of control triggers surfaced before they lapse

Case law citation networks

Cases cite cases; citations form a graph. Rank by centrality to find leading authority, walk citation paths to find controlling precedent, and detect when a case has been implicitly overruled by a downstream chain.

•Faster identification of controlling authority in unfamiliar jurisdictions
•Detection of subtle negative treatment beyond keyword flag services
•Citation graph aware retrieval for legal GraphRAG

Conflict of interest checking

Adverse party relationships, prior representation histories, and personal connections form a network. A graph lets conflicts teams run transitive checks in one query: including the corporate family relationships a name only check misses.

•Transitive conflict detection across corporate parents, subsidiaries, and affiliates
•Explainable results: the exact path that triggered the flag
•Faster matter opening turnaround

E discovery entity resolution

Custodians, aliases, email addresses, and phone numbers proliferate across sources. Resolve them to canonical people and organizations, then run graph queries to see who talked to whom about what, when.

•Higher recall on privilege review with fewer false positives
•Communication network diagrams for depositions and trial
•Reduced hosting and review costs from better early case assessment culls

Legal GraphRAG for research and drafting

Ground a legal LLM assistant in your matter files, the firm's precedent library, and licensed case law sources. Every answer cites the exact case, page, or precedent clause it came from: no fabricated citations.

•Zero hallucinated case citations: retrieval is constrained to real sources
•Faster memo and brief drafting with paragraph level citations
•Attorney review time on first drafts significantly reduced

Matter and client 360

One canonical view per client and matter, linking billing, documents, communications, deadlines, and involved personnel. The foundation for both partner level insight and any downstream AI.

•Real time matter status without asking the associate on it
•Client portfolio analytics across practice groups
•Trigger based automation (deadlines, court filings, engagement anniversaries)

Frequently asked questions

How do you handle privileged and confidential data?+

Every deployment is designed for legal industry confidentiality: matter level access controls at the graph layer, encryption in transit and at rest, on premises or private cloud deployment, no data sent to public LLM APIs when the matter requires it (open weight models in your VPC), and full audit logging of who queried what.

Can this integrate with our document management system?+

Yes. We build ingestion connectors for iManage, NetDocuments, SharePoint, and Relativity. The graph references documents by DMS ID and version, so a query result always links back to the authoritative document: nothing is duplicated or moved permanently unless you want it to be.

Which case law sources can we ground the LLM in?+

Any source you have a license or subscription for: Westlaw, Lexis, Fastcase, Bloomberg Law, CourtListener for public opinions, plus your firm's own precedent and know how library. We do not ship case law data itself; we build the retrieval and grounding layer on top of your licensed access.

How is this different from off the shelf legal AI tools?+

Off the shelf tools are trained and retrieved against a generic corpus. Legal GraphRAG on your matter files means the assistant answers using your firm's precedents, your executed contracts, and the specific matter's documents: with citations to those exact sources. That's the difference between a bright first year and a research tool.

What's a realistic first engagement?+

Two common starts: (1) a 10 week contract graph build over your executed agreements corpus with a playbook deviation dashboard, or (2) a 12 week legal GraphRAG proof of value on one practice group's research or drafting workflow with an evaluation harness for citation accuracy.

Service