I lead data and AI engineering: the modernization of an organization's data platform, the internal applications built on it, and the governed AI practice the team works in. I am based in the Philadelphia suburbs.
This site records the reasoning behind that work: what got built, what got rejected, what I got wrong, and what I still have not solved.
The idea behind all of it
Every business exists to serve people. That is the essential, the work people came to do and care about. Around it sits the must, the layer that keeps an organization fundable, auditable, and alive: documentation for customers and audits, the trackers, the status reports, the training material, and the paperwork that makes billing possible. Nobody joins a service organization for that layer, which is why it is everywhere late, thin, or missing.
AI is an accelerator, not a replacement. It absorbs that layer, so the same people deliver more of the mission while the machinery that funds it runs faster and more reliably. AI does not do the serving. It does the paperwork that funds the serving. The 12 projects below are the working proof.
The projects
Hover over a project for its one-line description, or see all 12 written out on one page →
The bigger picture
These 12 projects are not a set of unrelated builds. They form one modernization effort: extraction fixed before the warehouse it feeds, shared infrastructure before the applications that reuse it, and AI capability built as its own governed layer, with the same discipline pointed back at the AI's own work.
The reasoning behind that order, and the economics that changed which work is worth doing at all, are told in the bigger picture →