AI for Law Firms: Intake, Research and Drafting
Law is text-heavy, deadline-driven and expensive per hour — which makes it a strong fit for AI, and a dangerous one if verification is skipped. Here is where firms get value without risking their reputation.
- Published
Where AI helps
| Use case | Value | Risk level |
|---|---|---|
| Client intake | Structured matter summaries before the first call | Low |
| Document review | Flags clauses and deviations across large sets | Medium |
| First drafts | Notices, standard agreements from firm templates | Medium |
| Research assistance | Finds and summarises relevant material | High — must be verified |
The verification rule
Every citation an AI produces must be checked against the primary source by a lawyer. Well-publicised cases of fabricated citations in court filings show what happens otherwise. We build research tools that only cite documents they actually retrieved, link each claim to its source, and make the lawyer’s check part of the workflow.
Confidentiality
- Deploy in the firm’s own cloud or a private tenant.
- Use providers with no-training and data-retention terms that satisfy your obligations.
- Enforce matter-level access control in retrieval.
- Keep an audit log of every query and output.
A sensible first project
Intake is the safest start: an assistant that gathers facts from prospective clients, produces a structured summary and flags conflicts for checking. It saves partner time on every new matter and carries little legal risk.
Frequently asked questions
Can AI give legal advice to clients?
No. Client-facing tools should gather information and share general process information, not give advice.
Is our data used to train models?
Not with the providers and terms we use. We confirm this contractually per project.
Can it draft in our firm’s style?
Yes, from your own templates and precedents, with a lawyer reviewing every draft.
