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AI enablement for engineering teams
Your team has licences for four AI tools and no shared idea of what good use looks like.
Most AI rollouts stall at the demo. The tools get bought, a couple of people go deep, everyone else pastes code into a chat window and quietly concludes it is overrated. The gap is not the model. It is that nobody has defined what a reviewed, trustworthy, AI assisted change actually looks like in your codebase.
I work this from the inside, because it is how I work myself: a loop where one model sharpens the specification and interrogates the requirements, an agent implements against it, a second model reviews the diff, and a human owns the last mile. That loop is teachable. I have taught it.
What you get
- An honest read on where your team actually is, not where the licences suggest they are
- A working loop of specification, implementation and review, adapted to your stack and review culture
- Guardrails: what AI may touch, what it may not, how a diff gets trusted
- Your strongest people turned into the ones who spread the practice, so it survives me leaving
Right for you if you have 5 to 50 engineers, the tools are already bought, and adoption is uneven.