Consultancy
AI adoption with a quality bar.
I advise teams that want AI in production — measured, evaluated, and owned — not another slide deck of use-cases.
Wedge
Why this practice exists
Generic AI consultants sell possibility. Quality engineers sell survivability. I combine both: co-founder of Masters of Testing, builder of live AI products.
Opportunity map
Rank use-cases by impact × data readiness × quality risk. Explicit “won’t do” list included.
Product & evaluation design
Architecture choices, success metrics, failure modes, and review loops before you scale spend.
Ship partnership
Retained decision support while your team implements — pressure-tested tradeoffs, weekly cadence.
Ideal client
Fit / no-fit
Fit: you have a real workflow, some data, and a team that can implement. You care about evaluation, privacy, and not embarrassing yourself in production.
No-fit: you want a magic model pitch, unlimited use-cases, or agency theatre with no owners. I will say no.
FAQ
Citation-ready answers
What makes this different?
Quality-first. Masters of Testing co-founder background + shipping AI products myself.
Who is it for?
Founders, operators, and technical leads past the toy phase.