Most of the work today is helping small companies get value out of AI in ways that actually fit their team. Sometimes that looks like a prototype delivered in twelve weeks; sometimes it’s a weekly retainer where a whole engineering team learns which tools deserve a seat at the table and which ones don’t. The underlying instinct is the same in either case — build the smallest thing that answers the real question, keep the feedback loop short, and let the team learn on their own code, not on a demo project.

The tools change quickly, so the judgement matters more than the label on the box. Cursor, Copilot, Claude, ChatGPT, Gemini, and the prototype-first generators all earn their place in different contexts. Knowing which one to reach for, and when to put it down, is most of the job.

Twenty years in the background. Before the current pivot, the last two decades were spent leading teams through complex systems work — large-scale payment platforms, healthcare integrations, legacy modernization efforts, and high-volume consumer applications. Industries span payments, finance, healthcare, and entertainment. Architectures span monoliths, microservices, and the messy-but-useful middle.

That background shows up in the advisory work as pattern recognition — the kinds of decisions that look cheap today and expensive in eighteen months, the difference between a vendor choice that compounds and one that traps, the hiring calls that are hard to undo. It also shows up in the mentoring work, because most engineers benefit more from a senior peer asking the right question than from a novel framework.

How the engagements tend to feel. Direct, written down, unhurried. Problems get named plainly. Opinions are offered when asked. Decisions are recorded so the reasoning survives a handoff. Work happens in the open — shared docs, shared repos, shared weekly demos — so clients always know what’s in flight.

If that sounds like the kind of help you’re looking for, the fastest path is a twenty-minute intro call.