Contract review and clause extraction
Pull specific clauses, obligations and deviations from a standard across a contract portfolio, with the exact source passage attached to every finding.
AI & RAG Consulting for Law Firms and Legal Teams
Retrieval over contracts, case law and precedent for law firms and in-house teams, with citation accuracy treated as a hard requirement rather than a feature.
Frameworks I design against
- EU AI Act
- GDPR
- Legal professional privilege
- Bar association AI guidance
- Client confidentiality obligations
Short answer
Rubra builds retrieval systems for law firms and in-house legal teams: contract analysis, precedent search, regulatory monitoring and matter research, engineered so that every assertion carries a verifiable citation and the system refuses rather than guesses.
Last reviewed
Where I see this working
Precedent and matter search
Find prior work on comparable facts across the firm’s own document management system, which is institutional knowledge that currently depends on who happens to remember it.
Regulatory monitoring
Track changes across regulators and map each change to the client obligations and internal notes it affects.
Due diligence support
First-pass review across a data room, flagging what a lawyer needs to read rather than pretending to conclude.
Multilingual document review
Cross-lingual retrieval where a query in one language must return the relevant document in another, which is routine in Belgian, Swiss and EU institutional practice.
Legal is the sector where retrieval quality and citation integrity matter most, and where the consequences of getting them wrong have been most public.
Citations are a correctness requirement
In most applications, a wrong citation is embarrassing. In legal work it is sanctionable. So the system is built so that fabrication is structurally impossible rather than statistically unlikely:
- The model may only cite from passages actually retrieved in this request.
- Every citation is resolved and validated against the retrieved text before the response is returned.
- An unresolvable citation fails the response rather than degrading it.
- Refusal is a designed, measured behaviour. The system says the corpus does not cover a question rather than assembling something that reads as if it does.
Confidentiality has to be architectural
Matter-level access control is enforced at retrieval time, so a passage the user is not entitled to read is never retrieved, never enters the prompt, and cannot appear in an answer. Where the firm requires it, inference runs inside the firm’s own infrastructure and nothing leaves the tenancy.
Where the value is
The highest-return use case in most firms is not sophisticated: it is finding the firm’s own prior work. Decades of precedent, memoranda and negotiated positions sit in a document management system that is nearly impossible to search usefully, and the knowledge of what is in there lives with a handful of senior people.
Making that corpus properly retrievable is a lower-risk, higher-payback project than anything involving drafting, and it is the one that partners notice.
Frequently asked questions
How do you prevent an AI system from fabricating case citations?
By making citation a retrieval operation rather than a generation one. The system may only cite documents it actually retrieved, every citation is validated against the retrieved passage before the answer is returned, and any citation that cannot be resolved fails the response rather than being shown. Fabricated citations happen when a model is asked to recall from training; I never ask it to.
Is client-confidential material safe in these systems?
It is if the architecture is built for it: matter-level access control enforced at retrieval time, no data leaving your jurisdiction or your tenancy where required, contractual zero-retention with the model provider, and full audit logging of what was retrieved by whom. I have built systems where inference never leaves the firm’s own infrastructure. The safeguards need to be architectural. A policy that says do not paste client data into a chatbot is not a control.
Will this replace junior lawyers?
Not in what I have built, and I would be sceptical of anyone claiming otherwise. What it changes is the shape of the first pass: instead of reading two hundred documents to find the twelve that matter, a junior reads the twelve and checks the reasoning on the rest. The judgement work remains, and the verification burden is real. A system that produces plausible-looking output nobody checks is worse than no system.
Get a straight answer on your AI roadmap
A 30-minute call with the engineer who would do the work, not a salesperson. You will get an honest read on what is worth building, what is not, and roughly what it costs.
No NDA needed to talk. EU and UK hours in full, with afternoons overlapping US Eastern and Central.