Contract review agent · non-standard clauses + precedent comparison
A law firm with an M&A and finance client base. We built an agent that pre-reviews contracts, identifies non-standard clauses against the client's template, tags risks by category and compares them against the firm's own precedent library.
Corporate law firm (client under NDA)
Senior lawyers spent most of their time on the first read of long contracts: spotting deviations from the template, flagging risky clauses, drafting the review memo. Juniors could do it, but without the consistency or speed required. Every client had its own templates, playbooks and precedents — the knowledge was scattered across people's heads and folders, with no system. The firm's capacity was capped by senior lawyers' time.
Stack and deliverable
An agent specialized in Mexican commercial law: ingestion of the contract (scanned PDF, Word, Drive) and the client's template; clause-by-clause identification of deviations from the template, with a specific legal explanation; risk classification into the firm's standard categories (tax, employment, intellectual property, termination, indemnification, jurisdiction and governing law); comparison of the flagged clauses against the firm's precedent library — prior contracts vectorized with metadata; and delivery of a near-final review memo to the senior lawyer, who validates, adjusts and signs off.
What changed in the operation
What we learned
The firm's own precedent library was the decisive asset. Without it, the agent delivers generic output; with it, the output reflects the firm's style and standards. Building and curating the library took dedicated work — it mattered as much as the agent. It's the principle of proprietary data + a frontier model: neither one is enough on its own.
How it was built
Technology: Firm precedent library · OCR · Word/PDF · Audit log
Delivery: Staged delivery through to production
FAQ · this case
How did this Corporate law · M&A and finance project make it to production?
Staged delivery through to production: design, build, testing with real data and launch. By the end, the client's team was running the agent in production, with observability dashboards and guardrails already configured. Stabilization is part of the delivery.
What tech stack was used in this Corporate law · M&A and finance case?
Firm precedent library · OCR · Word/PDF · Audit log. Each piece was chosen to fit the client's existing systems — no forced migrations, integrating through official APIs wherever they were available.
How are results tracked?
Before the project starts, we agree on an operational baseline with the client, and during stabilization it's reviewed on the operations dashboard. Because of the NDA, we don't publish this client's figures. In the diagnostic we define what to measure in your case.
Can this be replicated at another company in Corporate law · M&A and finance?
The pattern is replicable; the specific implementation depends on the current stack, operating volume and internal rules. The Studio diagnostic identifies which pieces apply to your context and estimates the return for your case.
Who runs the agent after the project?
The client's team. Studio hands over the agent running and trains the team on monitoring, prompt adjustments and exception handling. Stabilization support is included for the first few months; after that, an optional monthly retainer is available.
What happens if the AI model changes or gets more expensive?
We don't depend on a single provider. If a model changes or gets more expensive, we move the agent to another one without rebuilding it.
Facing similar challenges in Corporate law · M&A and finance?
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