Ship at startup speed. Trust every line — even the AI-written ones.

I work in your codebase, not in slides. We solve your real problems live, together — ensemble style — on the work your team would be doing anyway. No workshops, no offsites, no homework that piles up. The habits stick because they were built in the actual work.

No — that's the whole point. The work we do together is real work that ships. There's no productivity freeze. Teams I coach typically end the engagement shipping more often, not less, because the fire drills shrink and reviews stop being a bottleneck.

We build review habits, fast feedback loops, and test patterns that catch the specific ways AI output goes wrong — plausible-looking code that's subtly broken, hidden assumptions, repeated mistakes. The team learns to use AI faster while shipping fewer surprises to prod.

Concrete operational metrics: lead time (commit → users), release frequency, percentage of releases that ship bugs, and team sentiment. At Flexio: lead time went from ~2 weeks to minutes, releases from one every two weeks to multiple per day, buggy releases from ~50% to ~10%, sentiment from 4.25 to 5.98 on a 1–7 scale.

You own the practices, not me. The point is the team becomes the asset. I aim to make myself unnecessary — clients usually extend anyway, but because they want to, not because they're stuck.

Ensemble (or mob) programming means the whole team works on the same problem together, on one machine. It's how the coaching transfers — patterns, judgment calls, and review instincts get passed across the team in real time instead of stuck in one engineer's head. It's especially useful in the AI era, where knowing which AI output to trust is itself a skill that has to be taught.

Book a working session — we'll spend the call on a real problem in your codebase so you can see how I work before committing to anything.