Surface Contract Risk in Minutes with AI Analysis

Yes, when built for confidentiality. We deploy in private or zero-data-retention environments so agreements never leave your control or train an external model. Contract confidentiality is a design requirement from the first line, because a leaked agreement is a far worse outcome than a slower review.

A lawyer decides. The system flags risky or missing terms against your playbook and cites the exact clause, but a person confirms or overrides every finding. It is a fast, consistent first pass, not a substitute for legal judgment, and the lawyer stays accountable.

Not if it is grounded, which is how we build it. Every flag points to a real clause in your document with a citation, and the system says so when a term is absent rather than inventing one. A finding with no citation is unusable, so we design against it directly.

Yes, with layout-aware parsing tested on your messiest documents. Scanned agreements, stamps, and unusual templates break naive tools, so we evaluate on your real contracts, not clean samples. A pipeline that only handles tidy PDFs fails on exactly the agreements that carry the most risk.

You do. Your contracts, the trained system, and the code remain yours, under your confidentiality terms. We build on your stack and hand over documentation, so there is no lock-in to us in what we deliver.

There is no single price; cost tracks scope. A focused build around clause extraction and risk flagging is a modest, weeks-long project, while a wider rollout across your contract and document systems is larger. We scope from one use case, quote a fixed range up front, and sequence so early value funds the next step rather than pricing everything at once.

The return comes from faster review, consistent risk flags, and fewer missed terms, set against build and running cost. It only holds when the model targets a real, measured cost, so we baseline first and report value against it. We would rather size the return honestly on your numbers than quote an industry average that may not fit you.

A focused pilot usually reaches a working version in a few weeks, then tuning on real data, with wider rollout taking longer. We start narrow, prove the numbers, and extend, so you see value early instead of waiting months for one large launch.

Common ones are clause extraction, risk flagging against a playbook, renewal tracking, and contract question answering. The best first project is the one tied to your biggest measurable cost or opportunity, not the most advanced-sounding option. We help you pick the use case where value is fast and the data already supports it.

We start with a short feasibility check on one use case: does the data exist, is it usable, and does it hold the signal the model needs. Often you already have more usable data than you expect. We assess it before recommending any build, so the first step is a decision, not a commitment.