AI Engineering for Legal Teams
Language models chosen on evidence from your own documents, client material that stays where it must, and evaluation that keeps running after go-live.
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Tell us the workflow
Law firms and in-house legal teams bring us the engineering behind their AI tools: which model to use for document review, contract analysis or legal research, how to test it for citation accuracy and fabricated authority, how to keep privileged or restricted material off shared infrastructure, and how to show an approver that quality has held since launch.
We do not give legal advice; we engineer and evaluate the systems your lawyers use.
Where We Help
Selection, Confidentiality and Evaluation
Separate engagements, each scoped to your matters and your tools.
Model Selection
Trust
Task-specific evaluation on your own material to choose a model on evidence.
Confidential Routing
Trust
Policy-enforced routing that keeps restricted matters on-premises, with a log of every decision.
Ongoing Evaluation
Reliability
Shadow and A/B evaluation so quality regressions surface before users find them.
How It Connects
Model choice lands in the LLM Selection Pack; approval evidence in AI Governance and Trust; ongoing evaluation in the Production AI Monitoring Harness. The underlying engineering is Generative AI.
We have not published a legal-sector case study yet. Tell us the workflow you are evaluating.
Legal AI Engineering FAQ
Which language model should we use for document review or legal research?
The one that does best on your documents and your tasks, at a cost you accept. We build a task-specific evaluation from your own material (citation accuracy, fabricated authority, missed clauses), run the candidate models against it and set out the cost-quality trade-off, so the choice rests on evidence rather than a vendor's demo.
Can restricted client matters be kept on-premises while other work uses a cloud model?
Yes, with a routing layer that enforces the policy rather than relying on users. We design and test data routing that sends restricted matters only to on-premises inference and logs every decision, so you can show where each document went.
How do we show search quality has not dropped after months of updates?
By keeping the evaluation running. We set up shadow and A/B evaluation for prompt, index and model changes, so a regression in retrieval or answer quality shows up before users notice it.
Working With Legal Teams
We work alongside your knowledge-management, IT and risk teams; we do not replace them. Each engagement ends in something your team keeps: the evaluation set, the routing policy, the monitoring.
