The engineer who teaches.

The two halves of the practice are inseparable. Every audit ends as a teaching moment for the team that has to maintain what gets shipped, and every workshop is grounded in a system that someone — usually me — actually built and watched run in production. I don’t trust AI explainers from people who only ship slides, and I don’t trust AI builds from people who can’t explain them to a room of operators.

My best engagements are with founders and ops leads who already know which step in their workflow is bleeding time. They need someone to name the bottleneck, sketch the architecture, and ship the system that retires it. Three shapes of work: a workflow audit (one-page hypothesis, two-week turn), a workflow build (production system, six to twelve weeks, observable from day one), or a workshop where your team learns to do the same exercise on the next workflow without me in the room.

I work mostly with teams across South Asia and the GCC. The North American AI playbook does not translate cleanly into a Karachi accounts team or a Lahore marketing studio — not because the technology is different, but because the workflows, the labor economics, and the operator instincts are. That gap is not a translation problem; it’s a design problem. Closing it is most of the work.