Legal AI solutions

AI for legal work where the source matters as much as the answer

Build secure research, document, and knowledge workflows that retrieve the right material, preserve matter boundaries, show citations, and make uncertainty visible.

Discuss a legal AI use case

Source-connected

Permission-aware

Measured before launch

Evidence-first designMatter-aware permissionsReviewable outputs

01

Ground legal AI in the record

Legal work demands traceability. A fluent response without reliable authority can create more work and risk than it removes. We design legal AI applications to expose the supporting text, distinguish internal and external sources, preserve metadata, and support professional review.

The objective is not autonomous legal judgment. It is faster access to relevant material, more consistent first-pass analysis, and better use of institutional knowledge within clearly defined controls.

02

Legal workflows suited to grounded AI

✓

Matter and precedent knowledge search

✓

Contract clause retrieval and comparison

✓

Discovery document triage and issue exploration

✓

Policy and regulatory obligation search

✓

Chronology and document-set summarization

✓

Research synthesis with source citations

✓

Approved drafting assistance and playbook guidance

✓

Client or internal intake classification and routing

03

Controls designed around legal information

Architecture can enforce client, matter, role, repository, and document-level restrictions. Evaluation should include citation correctness, source completeness, unsupported assertions, refusal behavior, and performance on adversarial or ambiguous questions. Logs and feedback must support review without exposing information to unauthorized users.

04

A bounded first implementation

Begin with one practice area, document class, or internal knowledge domain where authoritative sources can be identified and expected answers can be reviewed. A focused scope creates credible quality evidence before broader deployment.

AI security and governance →RAG evaluation →

05

Evaluation for legal retrieval

A legal test set should cover known-item retrieval, issue spotting, similar-language clauses, controlling versus noncontrolling sources, dates, jurisdictions, versions, privilege boundaries, unsupported questions, and citation precision. Reviewers should record both useful evidence and material omissions.

RAG evaluation →AI security and governance →

Questions

Frequently asked questions

RAG can retrieve relevant clauses, cases, matter documents, policies, or internal guidance and provide that evidence to a model for a cited summary, comparison, or draft.

It can when identity, source permissions, matter boundaries, retrieval filters, model handling, logs, and administrative access are designed and tested together.

Evaluation should cover retrieval completeness, citation accuracy, factual support, jurisdiction or date sensitivity, refusal behavior, and usefulness under professional review.

A practical next step

Bring us the workflow that is stuck—not a finished AI specification.

We will help clarify the opportunity, data, risks, and smallest useful way to prove value.

Plan a project