Legal & Corporate Governance AI Engineering

Yes. We deploy in private or zero-data-retention environments so privileged material never leaves your control or trains an external model. Confidentiality is a design requirement from the first line, not a setting toggled later, because a single leaked document is a far worse outcome than a slower workflow.

We ground every answer in your own documents and require citations, and the system refuses when it cannot find support rather than filling the gap. Invented citations are the known failure of legal AI, so we design against them directly and keep a lawyer reviewing before anything is relied on.

No, and we would not pitch it that way. It removes the slow, repetitive parts of review and research so people spend time on judgment and strategy. The lawyer stays responsible for the work; the AI is a fast first pass that a professional checks, not a replacement for professional judgment.

Yes, with the right parsing. Scanned filings, stamps, and non-standard templates break naive tools, so we use layout-aware extraction and test on your messiest documents, not your cleanest. A pipeline that only handles tidy PDFs fails quietly on exactly the documents that matter most.

You do. Your documents, the trained system, and the code remain yours, in your environment, 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 single contract review build is a modest, weeks-long project, while a wider rollout across your document and matter 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 hours saved on review and research, with fewer missed clauses, 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 on one use case such as contract review usually reaches a working version in a few weeks, then tuning on real data. Wider rollout takes 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 contract review and clause extraction, cited legal research, e-discovery triage, and intake automation. 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.