Most AI strategy work ends at a roadmap. Ours ends at a working prototype your operating team can react to — because the same people who modeled the business case also wrote the code.
We sit with the people whose KPIs are on the line. We pressure-test assumptions against your actual data. We tell you when the answer is "buy this, don't build it" — and when the answer is "neither."
Each one is fixed-fee, fixed-scope, fixed-timeline. No T&M creep.
Two-week intensive to identify the three highest-leverage AI bets in your business, with ROI math attached. Output: a prioritized portfolio and a build/buy decision.
For teams stuck with a model that "worked in the notebook." We harden it, evaluate it honestly, and put it in front of real users with monitoring, rollback, and a clear KPI.
A senior practice lead embedded one or two days a week — running your AI roadmap, vetting vendors, and mentoring your team until you're ready to hire a full-time one.
Use-cases ranked by net present value, risk, and time-to-impact. With the math behind each number.
A reference architecture for each top use-case — components, data flows, vendor choices, and integration points.
Honest recommendation per use-case. We tell you when an off-the-shelf tool wins. We also tell you when none of them do.
Who you need internally, when, and at what seniority — to own each system after we hand it over.
Pre-set KPIs, baseline measurements, and a dashboard scaffold — so the value of each project is visible from day one.
The boring document that saves projects. Data, regulatory, vendor, and adoption risks — with mitigations.
A decade of engagements means we don't start from zero in these sectors.
Half an hour with a principal will tell you whether it's worth a sprint — or whether to skip it.