I take messy, real-world problems and turn them into systems people actually use: warehouse digital twins, multi-agent workflows, and the RevOps automation that ties it together. Full implementations, not slide decks.
End to end, from the data model to the deployed app. The proof is something you can click, not a recommendation in a deck.
Multi-agent systems and automation that remove real work, designed to give honest outputs and flag what they do not know.
I teach the team to run it after I leave. Adoption is the deliverable, not a one-off demo.
A few builds that show the range. The full set lives on the Work page.
A walkable 3D digital twin of a ~75,000 sq ft fulfillment center, with light KPIs mapped right onto the floor. I modeled it like an apartment complex: every rack an address, every item a resident. The bridge between a dashboard and the physical space it describes.
Three AI "consulting firms" compete on the same brief, review and rework each other's output, then a partner agent synthesizes a board-grade report. ~26 agents in one run. It even caught and killed a flawed assumption in the original prompt.
A full learning platform that takes non-technical people from beginner to confident builder: structured modules, a knowledge hub, a workflow library, and progress tracking. Built to make AI adoption actually happen, not just get talked about.