Generative AI: Legal

Generative AI for legal teams

Legal work is mostly reading, comparing and drafting, which is exactly where large language models help, and exactly where a confident wrong answer does the most damage. We build legal AI that retrieves from your own documents, cites every source, verifies every citation and keeps a lawyer in control of anything that leaves the firm. Senior engineers own the design, AI coding agents carry the volume, and every line is reviewed by a human.

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Key highlights

We build contract review, legal research, eDiscovery assistance, intake and drafting tools on top of your own documents and precedents.
Every answer is grounded in retrieved sources and every case citation is checked against a real database before a lawyer sees it.
Matter level permissions and ethical walls carry through to the AI, so a user can never retrieve text from a matter they cannot open.
Models run through enterprise APIs with no training on your data, or self hosted in your own cloud when client terms require it.
A first use case, such as clause extraction for one contract type, usually reaches a guarded pilot in 8 to 12 weeks.

Why legal AI projects stall

Most firms have tried a chat tool. The stall comes when partners ask who is accountable for the output, where client data went, and whether the citation in the draft is real.

Citations that do not exist. General chat models invent plausible case names. Courts have sanctioned and publicly criticised lawyers for filing them, including Mata v. Avianca in New York in 2023 and Ayinde v Haringey in the High Court in 2025. One fabricated authority ends trust in the tool.

Privilege and confidentiality worries. Partners will not allow client documents into a tool unless they know where the data is processed, whether it is retained, and whether it could be used to train someone else's model.

Ethical walls the AI ignores. A search index built across the whole document management system can surface text from a conflicted matter. The AI layer has to respect the same walls as iManage or NetDocuments.

Knowledge locked in old matters. The best precedent clause is in a deal from six years ago, and only one partner remembers which one. Associates redraft from scratch because search on the DMS is keyword only.

Review volumes that outgrow the budget. Due diligence data rooms and disclosure sets run to tens of thousands of documents. Fixed fee work makes first pass review hours impossible to recover.

Pilots nobody can measure. A demo looks impressive, but without a test set of real contracts and agreed accuracy thresholds, the risk committee has nothing to approve and the project quietly stops.

Use cases

Generative AI use cases we build for legal

Each one is a narrow, testable system with a lawyer reviewing the output, not a general chatbot pointed at the firm's files.

Contract review and clause extraction

Document extraction pipelines pull parties, dates, governing law, change of control, assignment, limitation of liability and termination clauses into structured fields, flag deviations from your playbook and link every value back to the page it came from.

Cited legal research assistant

RAG over your precedent bank, know how notes and licensed sources, with answers that quote the passage they rely on. A separate verification step checks each case citation exists and says what the answer claims before it is shown.

Due diligence and eDiscovery assistance

Classification and summarisation agents run first pass review across data rooms and disclosure sets, propose relevance and issue tags, and route uncertain documents to reviewers. Integrates with Relativity or your review platform rather than replacing it.

Drafting copilot

Generates first drafts of NDAs, engagement letters and standard agreements from your own templates and clause library inside Microsoft Word, with tracked changes so the lawyer sees exactly what the model wrote.

Client intake and triage agent

A tool using agent collects facts from web or email enquiries, runs a preliminary conflict search, classifies the matter type and books the right fee earner, without ever giving legal advice to the enquirer.

Time narrative and billing assistant

Drafts time entry narratives from documents, emails and calendar activity on the matter, checks them against outside counsel guidelines and flags block billing before the bill goes out.

How we deliver

From one use case to firm wide adoption

01
Use case discovery and data readiness
We work with practice leads and IT to rank use cases by value and risk, then check the documents: OCR quality, DMS metadata, matter permissions and which sources the firm is licensed to use with AI.
02
Evaluation harness and prototype
Lawyers label a golden set of real contracts or questions. The prototype is scored on extraction accuracy, citation correctness and refusal behaviour on every change, so quality is measured, not argued.
03
Guarded pilot with a lawyer in the loop
A small group uses the tool on live matters. Every output is reviewed and corrected in the interface, and those corrections feed the test set and the playbook.
04
Production rollout with monitoring
Rollout to further practice groups with SSO, matter level access, usage and cost dashboards, drift alerts and a full log of prompts, sources and outputs for the risk team.

AI governance for legal

Professional duties do not change because a model drafted the text. The system has to make supervision, confidentiality and verification easy to prove.

Legal professional privilege Client confidentiality ABA Formal Opinion 512 SRA and Law Society guidance GDPR and UK GDPR EU AI Act transparency Matter level access control ISO 27001 aligned controls Self hosted model option

Confidential data stays under your control. We use enterprise model endpoints with zero data retention and no training on inputs, or open weight models self hosted in your own Azure or AWS tenancy and region when engagement letters or client outside counsel guidelines require it. PII and client names can be redacted before any external call.

Lawyers stay accountable, and the system shows it. Guidance such as ABA Formal Opinion 512 and SRA statements expects lawyers to understand and supervise the tools they use. Every output records the model, prompt, retrieved sources and reviewing lawyer, so the firm can show what was checked and by whom.

Citations are verified, not trusted. Generation and verification are separate steps. A citation that cannot be matched to a real authority in a source database is blocked or clearly flagged, and the tool says it does not know rather than filling the gap.

Models and tooling we use for legal AI

Model choice is made per use case after testing on your documents. The surrounding retrieval, permissions and evaluation layers are what make it safe.

Anthropic Claude OpenAI GPT models Azure OpenAI private endpoints Llama and Mistral self hosted LangGraph agents pgvector and Elasticsearch hybrid search Azure Document Intelligence and Textract OCR iManage and NetDocuments integration Relativity integration Microsoft Word add ins Langfuse tracing and evals Python and Node.js

FAQ

Common questions

How do you stop the AI from inventing case law?

By never letting the model be the source of truth. Answers are built from retrieved documents and quote them, and a separate verification step checks every citation against a real authority database before display. Anything unmatched is blocked or flagged, and the evaluation harness tests for fabricated citations on every release.

Is client data safe, and does it stay privileged?

Yes, if the architecture is right. We use enterprise APIs with no training on your data and zero retention where offered, or self hosted models in your own cloud region. Access follows your DMS permissions and ethical walls, and all data stays in your tenancy. Privilege questions about specific tools should still be confirmed by your general counsel.

How accurate is AI contract review?

Accurate enough to speed up first pass review on well defined clauses, not accurate enough to remove the lawyer. We measure precision and recall per clause type on your own contracts before pilot, set thresholds with the practice group, and route low confidence results to a reviewer.

Should we buy a legal AI product or build our own?

Buy when a product covers your workflow and your data terms. Build when you need your own precedents, playbooks, DMS permissions or client specific rules, or when you are a legal tech company shipping AI to your own customers. We often integrate bought tools and build the firm specific layers around them.

How long does a legal AI project take?

Usually 8 to 12 weeks to a guarded pilot for one use case. That covers discovery, a labelled test set, the prototype and a pilot with a small practice group. Firm wide rollout follows once the accuracy numbers satisfy the risk committee.

How is a legal AI project priced?

Usually as a fixed price discovery and prototype phase, then a pilot and rollout scoped from what the prototype measured. Ongoing model and hosting costs depend on document volume, and we estimate them per use case before you commit. See our pricing page for engagement models.

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