Law Firms
AI for the legal knowledge that doesn't require client consent to use.
Case law, statutes, firm publications, and public filings — structured into a research intelligence layer that compounds with every matter.
Outside law firms face a data rights problem that doesn't exist in other industries: the most valuable data they have — client matter files — is also the data they are ethically prohibited from using for AI training without client consent they rarely have. The right approach starts with what firms can use, builds real value there, and only adds client-confidential data when the ethics and consent framework supports it.
The data problem in legal — outside firms
Law firm data is uniquely constrained by ABA Model Rule 1.6, which prohibits disclosure of information relating to the representation without informed consent. This is broader than attorney-client privilege — it covers all information about the matter, regardless of whether it is publicly known. De-identifying client documents does not cure this problem the way HIPAA Safe Harbor cures it in medicine: the risk in legal data is the substance, not the identity.
Most engagement letters do not grant the firm rights to use client data for AI training or any non-representation purpose. Historical archives are effectively locked: the firm cannot use them to train models without retroactive consent programs that are impractical for large client rosters. Going forward, firms that modify engagement letters can start building a usable historical base — but that requires deliberate action today.
What firms can use is substantial but poorly organized. PACER filings, published opinions, public regulatory corpus, firm-authored CLE materials, practice group publications, and internal training materials represent a real and usable corpus — but it is scattered across document management systems, websites, and filing platforms without a unified retrieval layer.
Associates and partners at most firms spend significant time on work that the public legal corpus already answers: statutory research, case law synthesis, regulatory mapping, and cross-jurisdictional comparison. That time is expensive and the output quality is inconsistent. Infrastructure that indexes the public corpus to firm-relevant depth changes that calculus.
What we deliver
Synthetic data
Synthetic matter datasets with realistic procedural history, filing patterns, and entity structures — for training intake staff and testing matter management system configurations without using real client information.
Custom models
Research and synthesis models trained on the public legal corpus relevant to the firm's practice areas, plus the firm's own non-client content, tuned to the firm's research style and citation conventions.
Knowledge & retrieval
A structured index of relevant case law, statutes, regulations, agency guidance, and firm-owned content — queryable by matter type, jurisdiction, and legal issue, with citation-level retrieval for every retrieved passage.
See the full architecture for how these layers fit together.
Common deployments
Practice area research assistant
Research tool trained on the public legal corpus and firm publications for a specific practice area, answering research questions with citations to primary authority.
Jurisdiction mapping
Cross-jurisdictional analysis of statutes and regulations on a specified legal issue, comparing approaches across the states or countries relevant to the client's operations.
Matter navigation index
Metadata-only index of client matters — document name, date, author, matter ID — that tells attorneys which matter contains relevant prior work without synthesizing privileged content.
CLE and training content retrieval
Retrieval system over the firm's internal training materials, practice group presentations, and published client alerts, surfacing relevant internal expertise before external research.
Frequently asked
Discuss your legal — outside firms deployment
Tell us about your data, your constraints, and your workflows. We'll design the layers around them.
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