This post. An illustrative worked example, not a client result. The finance firm, its contract volume and its numbers are assumptions made for the walkthrough. It shows how AI contract review can work when a lawyer still signs off on every clause and every decision is logged.
The buyer in this example is a compliance lead at a regulated finance firm. Contract review is the bottleneck. Every incoming agreement goes to a reviewer who reads it clause by clause against the firm's standard playbook, then writes up where it drifts. The audit trail is a mix of email threads and tracked changes.
The design is a contract-review co-pilot that reads each contract, flags clauses against the firm's playbook, and hands a marked-up draft to a reviewer. For illustration, assume the firm measures review throughput and audit exceptions before the rollout and again after. This post shows what would change for the compliance team, not how it is wired. No real figures are claimed.
#What does AI for contract review actually change for a compliance team?
The co-pilot does the first read. It compares every clause against the firm's standard playbook and flags the ones that deviate, with the playbook rule it broke sitting right next to it. The reviewer no longer starts from a blank page and a 40-page contract. They start from a marked-up draft and confirm or overrule each flag. The same lawyers do the review. They now spend their hours on the clauses that need judgment, not on finding them.
What does AI for contract review change in a regulated firm?
A co-pilot reads each contract, flags clauses against the firm's standard playbook, and hands a reviewer a marked-up draft with a defensible audit log. A lawyer still approves every clause. The machine finds the drift; the person keeps the decision. The team would measure review throughput and audit findings before and after.
#How does this survive an audit?
Every ai-touched decision is logged. The log records the inputs, the model, the prompt, the output, and the reviewer who signed off. That means an auditor can pull any reviewed contract and reproduce exactly how a flag was raised and who cleared it. There is nothing to reconstruct from memory: the receipt already exists for every clause.
The design pairs the build with governance policy. The co-pilot is one half. The audit log that lets a regulator reproduce a decision is the other half, and the two belong together. A contract-review tool that speeds up the read but cannot show its work is a liability in a regulated firm, not a win.
- The co-pilot reads each contract and flags clauses against the firm's standard playbook
- Each flag carries the playbook rule it broke, next to the clause
- A reviewer confirms or overrules every flag before the contract moves
- Each decision logs inputs, model, prompt, output, and reviewer
- What to measure: review throughput and audit findings, compared before and after the rollout
We are
We design for a result you can measure, with a lawyer approving every clause and a log a regulator can reproduce.
We aren't
We are not selling an autonomous contract bot that approves clauses on its own and asks a compliance lead to trust it.
#Why call this a worked example?
Because no real client result sits behind it. The firm, the volumes and every number are assumptions, so you can swap in your own review backlog and your own last audit. We would rather show the mechanics and the receipts a compliance lead can check than quote an outcome we cannot document.
This design sits across our AI implementation and AI consulting work, and we write these up in the open in the journal. If you are near a buying decision on contract review and you want the audit log to hold, tell us what you're building. We will show you the log format before you commit to anything.
