AI by industry
AI for legal teams that shows its sources
We design legal AI for law firms, in-house legal departments and legal tech companies: contract review against your playbook, first drafts from your precedents, and search across matters and know-how. Every answer links to its source, and a lawyer approves anything that goes out.
01 /
Faster first passes, with lawyers in charge
- Claude
- OpenAI
- LangGraph
- Python
- FastAPI
- Elasticsearch
- Qdrant
- PostgreSQL
- Next.js
- Docker
Much of legal work is careful reading: checking a contract against a playbook, spotting the clause that changed between drafts, pulling key dates from a data room, finding the memo a colleague wrote on the same point last year. It is skilled work, yet much of it is a first pass that software can prepare. We build AI that does that first pass and shows exactly where each finding came from.
The standard in law is not a plausible answer but a verifiable one. So our systems retrieve from your own documents instead of relying on a model's general knowledge, quote the passage they rely on, and say plainly when the sources do not answer the question. Courts have sanctioned lawyers for filing citations that an AI invented, and firms are right to want tools that make checking easy rather than optional.
Confidentiality shapes the architecture from the start. Matter permissions and ethical walls carry through to every search result, client documents stay in environments you control, and model providers are engaged on terms that rule out training on your data. Lawyers remain responsible for the work, and the tools are built around their supervision.
02 /
Tools for contracts, drafting and knowledge
Each tool works from your own documents and precedents and returns its sources, so a lawyer can check the output quickly instead of redoing the work.
Contract review against your playbook
Compare incoming contracts with your standard positions and fallbacks, flag deviations, missing clauses and unusual terms, and suggest redlines from your approved language for the reviewing lawyer to accept or change.
Drafting from your precedents
Assemble first drafts of agreements, letters and routine filings from your precedent bank and the matter facts, following house style, with each clause traceable to the precedent it came from.
Matter and know-how search
Ask questions across past matters, research memos and practice notes in plain language, and get answers that quote and link the source, filtered by each user's matter permissions.
Due diligence review
Classify data room documents, extract parties, dates, change of control and assignment provisions, and build review tables that associates verify and supervising partners sign off.
Client intake and conflicts preparation
Collect the facts of a new inquiry through a structured form or assistant, identify the parties for your conflicts check, and route the matter to the right practice group for a lawyer to accept.
Deadline and obligation tracking
Pull renewal dates, notice periods, payment obligations and court deadlines from contracts and filings into your calendar or matter system, with the source clause linked for verification.
03 /
What the engagement produces
Private deployment
The application running in your cloud tenant or a dedicated environment, connected to your document management system with its permissions intact.
Structured playbooks and precedents
Your positions, fallback clauses and precedents organized so the AI can apply them, with a named owner on your side who keeps them current.
Accuracy results on your documents
Measurements against a review set your lawyers marked up, showing what the system finds, what it misses and where it should not be trusted.
Citation and audit trail
A record of every query, retrieved source, output and reviewing lawyer, available when a client, insurer or court asks how the work was done.
Guidance for your lawyers
Plain-language notes on what the tool is for, what it is not for and how to check its output, written to sit alongside your firm's AI policy.
Handover to your team
A walkthrough for your IT or innovation team covering the code, prompts, evaluation sets and configuration, so the firm is not tied to us.
04 /
From one practice group to wider use
We start narrow, with lawyers setting the standard the output must meet, and widen only when the results hold up.
01
Pick the matter type
We work with one practice group on one repeatable task, such as NDA review or lease abstraction, and document how lawyers do it today, what they check, and what a correct output looks like.
02
Build the confidentiality model
Matter permissions, ethical walls, retention and client consent requirements are mapped into the design, and we confirm which data may reach which model provider under your engagement letters and outside counsel guidelines.
03
Test against lawyer markups
Lawyers mark up a set of real documents, and we measure the system against their calls clause by clause. Errors are reviewed with the practice group, and prompts, retrieval or playbooks are corrected before the pilot.
04
Pilot under supervision
The practice group uses the tool on live matters with every output reviewed and feedback captured in the tool itself. Other groups follow once the pilot shows real time savings at an acceptable error rate.
05 /
Confidentiality, ethics and supervision
Lawyers' professional duties apply to AI-assisted work. In the United States, ABA Formal Opinion 512 and guidance from state bars address competence, confidentiality, client communication, candor to the court, supervision and reasonable fees. Clients add their own conditions through outside counsel guidelines, and GDPR applies wherever a matter involves personal data from Europe.
Your general counsel or risk committee decides how these duties apply to your practice. We make them practical to meet: privileged documents stay in environments you control, access follows matter permissions, outputs are drafts for a lawyer to review rather than advice to a client, and every step is logged. Nothing the system produces is legal advice until a lawyer has reviewed it and made it their own.
07 /
Related services
Legal software
We build matter management, CLM, e-discovery and AI-assisted research platforms used by law firms, in-house teams and courts.
Build RAG chatbot
You've seen the demos but your data is messy, multi-source, and updates daily.
Fix LLM hallucinations
Your LLM confidently invents facts — fake citations, made-up API responses, wrong product details — and users have noticed.
Build LLM evaluation pipeline
You ship LLM features by vibes — there's no automated eval, so model swaps and prompt changes are pure gut feel.
AI for fintech and banking
KYC documents, fraud signals and service in regulated finance.
AI for healthcare
Clinical admin, triage support and document intelligence.
08 /
Frequently asked questions
Is it safe to use AI with privileged client documents?
It can be, with the right deployment. Documents stay in your own cloud or a dedicated environment, model endpoints are configured not to retain or train on your data where the provider allows it, matter permissions are enforced and every access is logged. Your risk team reviews the data flows, and client restrictions become rules in the system rather than things people must remember.
How do you stop legal AI from inventing case law?
By never asking it to recall law from memory. Answers are generated from documents retrieved from your sources or a licensed research database, each statement links to the passage it relies on, and the system says so when the sources are silent. Citations are checked automatically against the retrieved text, and a lawyer still verifies anything that goes to a client or a court.
Can AI review contracts against our playbook?
Yes, and it is often the best place to start. We encode your standard positions, acceptable fallbacks and walk-away terms, and the system compares each incoming contract clause by clause, explains every deviation with the relevant playbook entry, and proposes language from your approved clauses. The reviewing lawyer accepts, edits or rejects each suggestion.
Does it work with iManage, NetDocuments or SharePoint?
Those systems offer APIs that an AI application can use to read documents while respecting their permissions, as long as your licenses and IT policies allow it. Connecting through them keeps documents in your document management system, and search results show only what each user could already open. We confirm the integration scope during discovery.
Should we build legal AI or buy an existing product?
Buy when an off-the-shelf tool fits your workflow, data and security terms; many do for general research and drafting. Build when the value lies in your own playbooks, precedents or client-facing process, or when AI must sit inside a product you sell. If a product you can license already solves the problem, we will say so during discovery.
Where should a law firm start with AI?
With one repeatable, document-heavy task in one practice group, such as NDA review, lease abstraction or diligence summaries. The work has a clear standard, lawyers can grade the output, and the confidentiality model built for it carries over to later projects. A short discovery sets the scope and the measure of success before the pilot begins.
Ready to build AI that actually works in production?
Tell us about your AI project and get a free technical consultation within 24 hours. We'll map your use case, assess your data, and give you an honest feasibility assessment — no sales pitch.