Cross-matter knowledge graph with source traceability. Find precedent across every matter your firm has ever worked.
Law firm knowledge is locked inside closed matters, departed attorneys' work product, and document management systems that only support keyword search. The strongest argument for a current motion may exist in a memo from three years ago, but nobody finds it because the terminology doesn't match and the new associate doesn't know to look. Firms lose and re-create work product constantly.
The strongest argument for your current motion was made in a memo three years ago — in a different matter, by an associate who has since left. The work product exists in the DMS. But keyword search doesn't return it because the terminology doesn't overlap. The new associate writes it from scratch, misses the strongest argument, and bills 12 hours for something that already existed. Across a firm with thousands of matters, this happens every week.
The problem is compounded by AI tools that hallucinate. According to a Stanford study, leading legal AI tools generate fabricated citations at rates between 17 and 33%. A fabricated case citation in a brief triggers sanctions, malpractice claims, and reputational damage. The fundamental problem isn't the language model — it's the retrieval layer. Most legal AI retrieves poorly, then generates confidently.
And every departure erodes the knowledge base further. Which judges favor which arguments, which deal structures worked in which contexts, which client preferences aren't documented — this institutional knowledge exists in people's heads and their email, not in any queryable system.
Anatypical builds a cross-matter knowledge graph that connects memos, briefs, contracts, correspondence, and court filings across all matters and practice groups. Every response includes source traceability via Glass Box — showing which documents were retrieved, which passages were used, and flagging any unsupported claims automatically. The hallucination rate isn't hidden; it's surfaced and quantified for every generation.
A query like "What arguments have we made about this doctrine in this jurisdiction?" returns results across matters, practice groups, and time — not just the current matter's folder. The institutional work product becomes discoverable regardless of when it was created or who created it.
Every response includes a source trail: which documents were retrieved, which passages were used, and a trust score reflecting confidence. Claims without source support are flagged automatically. An attorney can verify sourcing in seconds — click through to the exact passage — rather than re-reading entire documents.
The knowledge graph maps relationships between clients, counterparties, judges, jurisdictions, doctrines, and contract clauses. "What's our history with this opposing counsel?" returns every matter, interaction, and outcome — structured as a relationship map, not a document list.
When an associate leaves, their work product and the connections it established in the graph remain. The replacement inherits structured relationships, not just documents. The firm's knowledge compounds rather than eroding.
A litigation associate is drafting a motion to compel in a contract dispute involving a novel force majeure interpretation. They query Anatypical: "Have we argued force majeure in any prior matters involving this industry?"
The system surfaces a memo from a closed matter four years ago — different practice group, different partner — that made the exact argument they need, with case citations and a winning outcome. The memo was in the DMS the entire time, but keyword search never returned it.
The associate incorporates the analysis, cites the supporting cases, and completes the motion in three hours instead of two days.
Document management systems like iManage and NetDocuments are storage tools — they organize by matter, date, and author. They answer 'where is this document?' but cannot answer 'what does our firm know about this issue?' Legal AI tools like Harvey and DeepJudge improve generation quality but still depend on the retrieval layer. If retrieval is poor — and in a firm with thousands of matters, it usually is — the AI generates confidently from incomplete context. Anatypical fixes the retrieval layer itself.
