Somewhere inside most mid-market litigation practices, a partner or senior associate is quietly using a personal Claude or ChatGPT account to do real work — case law summarization, argument structuring, first-draft briefs. It saves them days. Nobody else can use it.
The job is not to invent new AI for your firm. The job is to find the AI your firm is already doing well, and turn it into a system the whole practice can use safely.
Numbers reflect a representative mid-market litigation practice at $1,000 per billable hour. Substitute your firm's rate and matter volume — the model holds.
One partner has been using a personal Claude account for business divorce analysis. It is saving him days per case. Everyone else at the firm is still doing the work the old way.
The reasons are familiar. There is no shared access. There is no integration with the firm's legal research databases. There is no validation framework, no audit trail, no policy on what client data can go in or out. An associate cannot use it on a Tuesday afternoon without scheduling time with the partner.
The firm handles roughly four business dispute matters per month. Each one needs case law research, argument structuring, and brief production. At $1,000 per billable hour, every hour saved on analysis is an hour freed for work that actually requires a lawyer.
The partner's prototype is the spark. We do not throw it out and start over. We take what is already working, harden it, and put it in front of the rest of the practice with the controls a law firm needs.
The pilot success metric is concrete and time-bound: three business dispute matters completed through the system by the end of month two. Either the firm hits that or it does not, and we both know.
Four matters per month. Ten hours saved per matter. At $1,000 per billable hour, that is $40,000 per month in capacity returned to work that actually requires a lawyer — depositions, client strategy, trial prep.
The pilot pays for itself in three matters. Once the pattern is proven on business divorce, the same shape extends naturally — wage and hour disputes, commercial real estate analysis, contract review. Each new practice area is a smaller engagement than the first because the platform is already there.
Sub in your firm's actual rate, matter volume, and hours-per-matter to model your own number. The defensible question for a managing partner is not whether AI can save lawyer time — it is whether the savings are worth more than the engagement.
Three phases. The first one tells you whether the rest is worth doing.
We find the AI work already happening inside the firm. Who is using what, where the real time savings are, and what is fragile or risky about the current setup.
We turn the strongest spark into a small production system: shared access, controls, integrations, and a pilot success metric the firm can defend.
Once one practice area is working, we extend the same pattern to the next — wage and hour, real estate, contracts. Each step is faster than the last.
That is the right instinct, and it is exactly what the hardening phase addresses. We build a data policy that defines what goes into the AI system, what does not, and where it lives. The Florida Bar's Advisory Ethics Opinion 24-1 requires attorneys to investigate any AI tool's data retention and sharing policies before use. We help you meet that standard — proper access controls, audit logs, and a workflow that keeps client data within systems you control.
Those are products. Harvey starts around $1,200 per seat per month with a 20-seat minimum. CoCounsel and Lexis+ AI are research add-ons bolted onto existing subscriptions. None of them audit what your attorneys are already doing with AI, build firm-wide controls around it, or help you operationalize it across practice areas. We are not competing with those tools — if one of them is the right fit, we can help you implement it properly. But most mid-market firms need the governance and change management layer first.
Validation is built into the system, not left to individual judgment. Every AI-generated output goes through a review step before it leaves the firm. The system is designed to assist with research and drafting, not to file anything autonomously. Florida's 11th and 17th Circuits now require attorneys to certify they independently verified all AI-assisted citations — the workflow we build makes that certification straightforward rather than burdensome.
More leverage, not less. Right now that partner is spending time being the AI help desk for anyone who wants to use it. Once the system is firm-wide, the partner gets their own time back and gets credit for the initiative that improved the entire practice. The hours they were spending on manual AI administration become hours available for client work.
Fixed-scope engagements with transparent pricing. Discovery and pilot typically run as a single engagement scoped to one practice area. No open-ended hourly billing, no retainer that drifts. The pilot pays for itself if it saves what the math section on this page says it saves — and you can verify that with your own rate and matter volume before you start.
The pilot has a concrete success metric: three matters completed through the system by the end of month two. If it does not hit that number, we both know. The discovery phase is designed to identify whether a viable pilot exists before you commit to building one. If there is no obvious win, we say so during discovery rather than selling you an implementation that will not land.
30 minutes. We look at one case analysis workflow and figure out where an agent can free up billable hours. If there is no obvious win, we say so.