INDUSTRIES / LEGAL & PROFESSIONAL SERVICES
AI for Legal & Professional Services
We build production AI for law firms where a hallucinated citation is malpractice — research, discovery, and drafting shipped with privilege boundaries, matter-level isolation, and every assertion resolved to a source a partner can open. The kind of system a risk committee signs off on.
Use cases: Multi-agent legal research; Discovery summarization; Citation verification; Semantic clause diffing; Litigation scenario modeling; Associate drafting support.
Compliance and constraints handled: Attorney-client privilege, Citation verification, Conflicts & ethical walls, Defensible audit trail, Human-in-the-loop, Client-owned deployment.
Can AI be trusted for legal research without inventing citations? Yes — when citation verification is built into the pipeline. Our legal work decomposes research into specialist agents and adds a verification step that re-reads each source passage and rejects any claim it does not support, driving hallucinated cites toward zero. Every assertion resolves to a source an attorney can open.
How do you protect attorney-client privilege? With matter-level access controls and ethical walls enforced at the retrieval layer. Content is indexed with matter-level ACLs so privileged material never crosses between engagements, and the system only sees what a given matter team is already entitled to see. Deployment lives inside the firm with SSO and existing permissions.
Will this replace our associates? No. It removes the four-hour research slog and the cite-checking grind so associates and partners spend time on judgment and strategy. In practice partners adopted fastest because they saw the leverage immediately — the lawyer still owns every conclusion and the final work product.
How long until a legal workflow is in production? A focused workflow typically goes from pilot to production in about six weeks: scope one practice group with a partner champion, ship a verified vertical slice, harden privilege and audit controls, then deploy and transfer the runbook. Starting narrow with a champion who can adjudicate disputed outputs is what keeps it on schedule.
How do you handle conflicts and ethical walls? Retrieval is entitlement-aware: ethical walls and matter-level permissions are enforced before the model ever sees a document, so a walled engagement stays walled. Every query is logged with a full reasoning trace, giving compliance a defensible record of what was accessed and why.
Which legal workflows are the best fit for AI? Workflows with high volume, a clear citation or defensibility requirement, and existing expert reviewers: multi-agent legal research, discovery summarization, citation verification, semantic clause diffing, litigation scenario modeling, and associate drafting support.
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