Contract review against your playbook
Standard agreements read against your own positions, with deviations flagged, the fallback position suggested and the clause cited - so the lawyer reviews exceptions rather than whole documents.
In-house teams are asked to cover more matters each year without more lawyers. The realistic gain is not machine-generated advice; it is removing the reading, routing and first-drafting that stands between a lawyer and the judgement only they can supply.
Not everything on this list will apply to you. Most organisations start with one and extend once it has been measured.
Standard agreements read against your own positions, with deviations flagged, the fallback position suggested and the clause cited - so the lawyer reviews exceptions rather than whole documents.
A structured front door for the business that captures the facts, classifies urgency and risk, and routes to the right lawyer with the context already assembled.
Answers grounded in your own executed documents and advice notes, with the source cited, replacing the search that currently happens by asking a colleague.
NDAs, DPAs, standard orders and routine correspondence drafted from your templates with the matter facts filled in, for a lawyer to finish.
Extracting the dates, notice periods and commitments buried in executed agreements into something the business can actually act on.
Reviewing invoices and matter narratives against scope and agreed rates, and making the spend picture answerable at board level.
Matter intake and triage is usually the cheapest high-impact change, and it produces the data that makes everything after it measurable.
One contract type, positions written down, review automated against them. It forces a useful conversation about what your positions actually are.
People are already using AI on your contracts. A sanctioned, grounded tool is a risk reduction, not an indulgence.
Retrieval is access-aware: a user can only ever be shown passages from matters they are already entitled to see, enforced in the system rather than by convention.
Every grounded answer carries its citation to your own source document, and the system is built to say "not in the material" rather than to improvise an authority.
Where privilege or client obligation rules out third-party processing, the deployment is private - in your tenancy or on your own infrastructure.
Two to four weeks to an evidenced picture of your AI use, spend and risk - and a ranked list of what to do first.