Every hour an associate spends on first-pass review or re-researching the firm's own precedent is an hour written off. We help firms reclaim that time with AI built private to the practice — and we've done it before, turning 43 years of one firm's legal data into a plain-language searchable platform.
The pattern repeats at most firms: associates grinding review, paralegals retyping intake forms, partners asking "haven't we briefed this before?" with no way to check. Legal-tech vendors flood your inbox, but ask one how they handle privilege, ethical walls, or conflicts data and the demo goes quiet.
We build for those constraints first — and we've shipped legal AI that passed that scrutiny.
Where AI Helps First
First-pass review, clause extraction, and summary memos on the document sets that swallow associate time — with a lawyer always making the call.
Every brief your firm has ever filed is precedent someone will re-research next month. Make the archive answer questions instead.
Screen inbound matters against your criteria and shrink the distance between a research question and a cite-checked starting point.
What We Do
A partner-level plan connecting AI to firm economics — where automation recovers write-off hours, speeds matter turnaround, and lets associates bill instead of grind — with confidentiality treated as a design constraint.
We sit with each practice group, trace how matters actually move — intake to research to drafting to filing — and rank the automation candidates by recovered hours and risk.
We ship the systems ourselves: contract and document review assistants, a firm knowledge base you can question in plain English, intake screening, research acceleration — deployed private to the firm.
Client files stay inside privilege. Models run where your IT dictates, see only the matters you authorize, respect ethical walls, and leave an audit trail — nothing about your clients trains a public model.
Proven in Legal
JVRC brought us four decades of matters, briefs, and records. We returned a secure platform their team questions conversationally — built with the access discipline privilege demands, by engineers with Fortune 50 enterprise AI backgrounds.
See customer resultsFAQ
A legal AI consultant has to speak both languages: what the technology can do, and what a firm's confidentiality duties, ethical walls, and billing model demand. We assess where AI recovers hours across review, drafting, intake, and research — then our engineers build it, integrated with the tools your firm runs, from your DMS to practice platforms like Clio, rather than referring you to a vendor.
We have. JVRC engaged us to make 43 years of accumulated legal data searchable, and we delivered a secure platform their team queries in plain language. That project taught us what legal-grade AI requires — provenance, access discipline, and answers a lawyer can verify.
Wherever the work is reading and retrieval rather than judgment: high-volume document and contract review, mining your own precedent, drafting first passes, screening intake, and accelerating research. The economics are direct — recovered associate hours either become billable or stop being written off.
Confidentiality drives the architecture. Systems deploy on infrastructure your firm controls, matter access mirrors your ethical walls, every query is attributable, and client data is never used to train shared models. We document the design so your GC or ethics counsel can interrogate it.
The audit is priced per firm after discovery — practice-group count and workflow complexity drive it — and builds are quoted as fixed scopes. Ranges are in our pricing guide; most firms weigh the figure against a single associate's annual write-off hours.
It depends on which workflow goes first, how fast the source data can be gathered, and your risk committee's review pace. Knowledge-base and review projects tend to show value early because the documents already exist — we validate on a contained matter set before widening access.
Bring a managing partner and one painful workflow. We'll walk through what AI would recover, how privilege stays protected, and what the first ninety days of a build look like.
Plan your AI workflow