Legal AI tested on your own matters, and Clio workflows built for your firm. Harvey, Legora, CoCounsel, and Clio's own AI all want a seat at your firm. We send an engineer who tests them on your past matters, sets up the one that fits, and builds the docket and Clio work none of them cover.
Bring 20 past research or drafting tasks and your Clio setup.
Published results in law firms
Two firms of very different sizes published results. Both did deliberate rollout work.
KMSC Law reports its lawyers save at least 3-5 hours a week with Harvey, and power users up to 10.
The firm added prompt-engineering training so lawyers in every practice area could see where it helped.
A&O Shearman's ContractMatrix, built on Harvey, cut contract review time by 30%, an estimated saving of up to 7 hours per review.
A ring-fenced sandbox and governance rules came first. The firm then built its own tool on Harvey's API and Azure OpenAI, grounded in its precedents.
Figures as reported by the vendor or customer at the linked source. They are not OpenNash results.
Where our engineer comes in
KMSC added prompt-engineering training so every practice area could use Harvey. A&O Shearman ran a sandbox and wrote governance rules before going firm-wide, then built its own tool on top. A 20-lawyer firm needs the same steps without a legal-tech team. Our engineer does that work on a firm-issued laptop, inside Clio and your document system.
| Decision | What we look at | Outcome |
|---|---|---|
| Which legal AI tool, if any | Harvey, Legora, CoCounsel, and Clio's AI on 20 of your past research and drafting tasks, with partners scoring the output. | A recommendation with per-seat cost. Sometimes the answer is a general assistant with firm rules. |
| What happens to client data | Training, retention, and logging terms for each vendor, read against your engagement letters. | A one-page summary your partners can sign off. |
| What to build | Clio matter updates, document naming and filing, and docket monitoring. | Built for your firm and run in your accounts. |
| What the AI may never do | Advice, filings, client contact, and final deadlines. | Written down as rules and tested before launch. |
Why forward-deployed engineers
Built for their largest accounts, deploying their own models. OpenAI's deployment company launched with more than $4B. ↗
Configure their own product, priced per seat or per resolution.
A small senior team, ex-Palantir, Meta, and Databricks. Any model, any vendor. Free pilot, then a flat monthly fee.
Even OpenAI closes the gap with engineers in the room. Source: OpenAI, What FDE means at OpenAI, Oct 2026.
The first eight weeks
Written down with your team in week 1. The pilot is free, and it has to pass these before anything goes further.
Example acceptance tests, not results from a customer deployment. Yours are written with your team.
Systems we work in
Rules from day one
FAQ
No. It researches, drafts, organizes documents, and monitors dockets. Attorneys approve advice, filings, deadlines, and client communications.
We document each service's training, retention, and logging terms before it sees matter data, and use zero-retention settings or a private deployment where required.
Maybe. We test them on your own past tasks first, and if one wins we help you roll it out. Docket and Clio work usually still needs building.
Why you need one
A senior engineer joins your team on a laptop you issue, tests the options on your data, and puts one law firms workflow into production in 4 to 8 weeks. It starts with a free 14-day pilot judged against test cases you agree in writing.
After launch, we run and improve the workflow on a flat monthly fee, or your team takes it over. The code, tests, and runbook are yours either way.
Bring 20 past research or drafting tasks and your Clio setup.