Your legal AI problem isn't strategy. It's delivery.
Most legal teams already know what they want from AI. What they don't have is the capacity to test it, the evidence to defend it, or anyone accountable for shipping it. That is the work we do — in fixed-fee sprints you can buy this quarter, and programs that follow when they earn it.
Start where your problem is
Departments and firms are solving different things with the same skills. Pick your side and the language stops being someone else's.
I run a legal department
The platform reckoning
Your incumbent bolted AI onto an architecture from 2014. The AI-native tools skip matter management, e-billing, accruals and spend reporting entirely. Your renewal is the moment to decide, and it's coming.
For legal departments →I run a law firm business function
Client pressure, and margin
Clients are asking what AI value you deliver and most have no way to see it. Meanwhile realization leaks through write-offs, aged WIP and a collection cycle that keeps stretching.
For law firms →
Built by someone who has sat in every seat
100+ yrs
Combined legal-domain experience across the team
Four seats
Engineer, vendor, advisor, operator — the whole buying cycle, from the inside
Platform-neutral
No platform to sell, no bench to feed, no quota
A fixed-fee sprint, bought this quarter
Two to three weeks, one approver, a deliverable you keep. Most of our programs start because a sprint showed what the real problem was.
AI Tooling Reality Check
You are paying for AI features across several platforms, and the module your incumbent added at renewal may or may not do the job. Nobody has tested any of it against your own matters.
- Inventory of licensed AI tools and modules, with cost and actual usage
- Overlap map showing where you are paying twice for the same capability
- Hands-on evaluation of up to three tools against a task set built from your work
- Scored results with the failure cases written down, not summarized away
- Keep, drop or renegotiate recommendation for each tool
Sprint fees are confirmed in writing when we scope the work, usually within one business day of your note.All sprints →
Built on the platforms your firm already trusts · governed end to end
- Microsoft Azure
- Claude
- Claude Code
- Copilot Studio
- Azure AI Foundry
- iManage
- NetDocuments
- Entra ID
- Microsoft Purview
- Agentic AI
- RAG & evaluation
- Responsible AI
- Power Platform
- Power BI
Evidence first. Then a baseline.
Five stages, and a loop: what we prove at the end becomes the baseline for what comes next. Nothing ships without a human-review design, you own everything we write, and every engagement leaves an instrument behind.
STAGE 1
Evidence
What is actually happening — in the data, in the contracts, and in what people do when nobody is presenting. We interview, sample and read before proposing anything.
STAGE 2
Baseline
Measure it before we change it: time, cost, error rate, review effort. A claim of improvement is worthless without the number it improved on.
STAGE 3
Design
Decide the approach and where a human has to be. Retrieval, routing, review points, privacy and IP positions — signed off by the people who own the risk.
STAGE 4
Ship
Build and release in slices, against an evaluation set, with guardrails and a way back. Something real is in use before the engagement ends.
STAGE 5
Prove
Report against the baseline, including the parts that did not improve. Then decide what is worth doing next, on evidence rather than enthusiasm.
Prove feeds the next Baseline.
Start with one sprint
Two to three weeks, one approver, a deliverable you keep. Tell us the problem and we'll come back within one business day with a scope, a date and a fee.
Scope a sprint →