For the past three years I have designed and deployed production AI inside live business
operations. Agentic workflows that act on real records in real systems of record and keep running after
handoff. That means the whole depth of the problem, not just the model: mapping the process, modelling the
data underneath it, designing the orchestration and integration layer, defining evaluation criteria before
launch, containerizing and deploying the services, and staying embedded until the client team can run it
without me.
What I have learned is that AI projects almost never fail on model capability. They fail on the process
nobody mapped, the data nobody cleaned, the failure mode nobody planned for, and the handoff nobody
designed.
That is where my background is an advantage rather than a detour. I spent 15+ years building revenue
engines across SaaS, fintech, and EdTech before I started automating them. So I scope against what a
business can actually absorb, not against what a model can theoretically do.
I work embedded. Discovery, data readiness, architecture, build, integration testing, enablement, handoff.
I would rather ship a working system and be measured on whether it holds up in month six than deliver a
recommendation and leave.