The engineer the AI learned from

I've spent 25+ years taking products from concept to polished, scaled, revenue-growing systems — prototyping, building, maintaining, scaling, and growing them. My AI practice works because I've taught my agents to build the way I build: my standards for architecture, testing, code quality, security, databases, and delivery are written down as a rulebook and a library of 30+ reusable skills my agents follow on every task.

Everyone has access to the same AI models. Almost nobody has 25 years of full-lifecycle product experience poured into them. You're not hiring someone who uses AI tools — you're hiring the engineer the AI learned from.

That changes the process itself. I no longer run a waterfall of heavy upfront phases: AI lets me prototype in hours, so plans are validated against working software from the first week, and the loop below runs continuously until launch — and after it.

And it keeps learning. My review is where the practice compounds: every correction I make becomes a pattern, and every recurring pattern gets written back into the standards my agents work from — so the team that builds your month-six feature is sharper than the one that built week one.

Discover & Frame

1 Discover & Frame

We start with your goals, constraints, data, and budget. Every open decision is surfaced and resolved up front — my agents execute from decision-complete plans, so ambiguity never stalls a build.
Prototype Early

2 Prototype Early

Working software in days, not decks: AI-accelerated prototyping puts a clickable, testable slice in your hands almost immediately, so we validate direction before investing in depth.
Build in Parallel

3 Build in Parallel

Independent workstreams are built in parallel by coordinated AI agents in isolated branches — each following my written standards for architecture, code quality, and security — then integrated and reviewed by me.
Verify Relentlessly

4 Verify Relentlessly

Every change is test-driven and ships as a reviewable pull request, confirmed green in CI before it merges. Money-handling and data-critical logic is built test-first against worked examples and verified against a live stack — not just mocks. Nothing reaches you until I've reviewed it myself: green tests are the floor, not the sign-off.
Ship & Iterate

5 Ship & Iterate

Small, reviewable releases land continuously, with durable project memory carrying context between sessions — so month six moves as fast as week one, with no quality cliff. Then the loop repeats: measure, learn, build again.

What makes it hold up

  • A written operating system: verification discipline, TDD by default, atomic commits, CI-gated pull requests
  • 30+ authored skills encoding my standards across the whole product lifecycle
  • A managed AI team — planner, implementer, independent reviewer, asset generator — each seat filled by the AI model family best suited to it, so reviews never share the author's blind spots
  • A human sign-off on every delivery — and every correction fed back into the agents' standards
  • Work is routed to right-sized AI automatically — senior-model judgment where the stakes are high, faster models for the mechanical work
  • Proven across stacks: TypeScript/NestJS, Vue, Go, Ruby on Rails, PHP/Laravel, R/Shiny, Python/FastAPI
  • Persistent project memory and clean handoffs — no quality cliff on long projects
  • Speed from parallelism, not corner-cutting

AI is a force multiplier on a senior engineer's process — you get full-team capabilities at the speed and cost of one focused expert.