Technical Journey

Over my career I've built a strong foundation in computer science, systems design, and the architecture of complex business platforms.

In the '90s I was a Mac developer writing C — publishing tools and printer drivers at a systems integrator serving the print and media industry. In the early 2000s I built web tools in PHP, advanced to Chief Technology Officer, which led to running internet businesses, which led to private equity.

For the next fifteen years my technical work happened at the systems level: designing the reporting infrastructure, data flows, and integration architecture that let a 15-unit, $180M operating platform run as one business. Six acquisitions, each one a systems-integration problem — banking, payroll, insurance, and management platforms migrated onto shared services.

That's the background I brought to AI: not a coder's, an architect's.
When the current generation of AI tools arrived, the gap between designing a system and building one collapsed. Decades of pattern recognition — how data should flow, where a process will break, what an integration actually costs — now translate directly into working software. I design the architecture and the implementation follows, with AI doing the labor that used to require a team.

The result is a body of production work, not experiments. Multiple deployed MCP servers powering enterprise AI integrations and async human-AI workspaces. A buyer-intelligence platform that turns 160 million words of executive-interview data into queryable insight. Native Mac and iOS applications with embedded AI collaboration. Multi-model architectures where cost tracks task complexity — the expensive model only runs when the problem deserves it.

The hard part of AI was never the code. It's knowing how a business actually works — where the real process lives, which data matters, what breaks when you change it. That's systems design. Now the tools keep up.