People who care.
Solutions that solve real problems.

“I don’t use AI to write code for me. I direct it like a team, building context-aware, self-correcting tools and pointing them at jobs too large for one person to hold in their head: sweeping upgrades, full-codebase audits, real-browser verification. The gap between output that looks right and work you can rely on is where I spend my time, turning open questions into decisions backed by evidence instead of opinion, including testing my own assumptions and saying so when I’m wrong.”

“As a Delivery Lead, much of my work is about keeping complex, multi-stakeholder projects moving without losing precision. Applying AI tooling to streamline resourcing and reporting workflows has let me spend less time on manual coordination and more time on what drives client outcomes.”

“I work on two systems that share nothing: a registration platform stitched into a decade of legacy services, and a workforce monorepo where legacy core and modern microservices run side by side. My AI workflow transfers between them, since the investment is the same either way: I document how each environment actually works, including the undocumented parts (which service misreports errors, where logs live, what a release involves). The model then starts every task with context a new teammate would need a month to build. The payoff: investigations that used to cost an afternoon across CI, metrics, and logs now run as one command and return a root cause I can argue with.”