AI development
for legal.
Legal work is a strong fit for language models and an unforgiving one for hallucination. The difference between a useful legal AI system and a liability is whether every assertion traces to a source a lawyer can check in seconds. We build retrieval-grounded systems for exactly that reason.
Discuss your projectHigh-value use cases.
Contract review
Extracting parties, dates, obligations, and non-standard clauses against a playbook. High volume, well-defined output, and verifiable against the source text — the strongest use case in the sector.
Clause comparison
Flagging deviation from standard positions across a contract portfolio, which turns a manual sampling exercise into full coverage.
Document-grounded research
Retrieval over your own matter files, precedents, and know-how using RAG so every answer carries citations to the source document.
Due diligence
First-pass review across large document sets, surfacing what needs human attention and producing an audit trail of what was examined.
Obligation management
Extracting deadlines and covenants into a structured register, converting contracts from documents into monitorable data.
Intake and triage
Routing matters and generating structured summaries so specialist time is spent on substance rather than sorting.
Sector-specific constraints.
Generic AI advice fails here for specific, predictable reasons. These are the constraints that shape every design decision we make in legal.
- Citations are mandatory — an assertion without a checkable source is unusable; ungrounded generation is a liability
- Privilege and confidentiality — matter data controls where models run and what may reach a third-party API
- Negation and scope — 'shall not' and 'shall' sit close in embedding space and are opposite in effect
- Jurisdictional variance — the same clause language carries different consequences by governing law
- Long documents — agreements exceed practical context windows; chunking strategy determines quality
- Professional responsibility — the lawyer remains accountable, so the workflow must support genuine review
AI in legal.
How do we stop a legal AI system from inventing case law?
By removing the opportunity rather than instructing against it. The system should answer only from retrieved documents, return the passage it relied on alongside every assertion, and explicitly decline when retrieval finds nothing relevant. Verify that refusal behaviour with adversarial testing — ask questions whose answers are absent from the corpus and confirm the system says so. Never rely on a model's own knowledge of authorities; retrieve them from a source you control.
Can we use AI on privileged client documents?
Yes, with the right arrangements, and the analysis should happen before any prototype touches real matter data. That generally means a contractual guarantee that data is not retained or used for training, a documented data flow, and consideration of client consent and any outside-counsel guidelines that apply. Where those cannot be satisfied, running an open-weights model inside your own environment keeps documents within your control boundary.
Is contract review AI accurate enough to rely on?
For extraction and flagging against a defined playbook, current systems are good enough to change how the work is done — as a first pass that a lawyer verifies, not as a replacement for judgement. The right framing is recall-oriented: the system should surface everything that might matter, accepting some false positives, because a missed non-standard indemnity is far more costly than an unnecessary flag. Measure it on your own contracts before trusting any vendor benchmark.
Other industries.
Healthcare
Custom AI and ML development for healthcare — clinical decision support, imaging, and documentation, built for HIPAA, audit, and c…
ReadFinancial Services
Custom AI and ML for banking, payments, and asset management — fraud detection, credit risk, and document automation built for mod…
ReadLogistics & Supply Chain
Custom AI and ML for logistics and supply chain — demand forecasting, ETA prediction, routing, and exception management built on m…
ReadLet’s scope it
properly.
Tell us the problem you are trying to solve in legal and we will tell you honestly whether machine learning is the right tool.
Start the conversationor write to us at [email protected]