Overview
I ship AI features and then prove they work. This documentation is the honest, complete version of what I build, how engagements run, and the method behind the proof.
#The thesis
Shipping an AI feature is the easy 80%. The hard 20% — the part that decides whether it survives contact with real users — is proving it behaves: that a prompt change didn’t quietly regress, that it won’t leak data or get jailbroken, that it stays in its lane. I build both halves, and I treat the proof as a first-class deliverable, not an afterthought.
The through-line on everything here is proof, not vibes: every capability links to a real artifact — a public repo, a verbatim test run, or a live screenshot — and this very site runs its own QA suite on every deploy.
#Who this is for
Teams shipping LLM features
You have a chatbot, agent, RAG system, or generator in production (or nearly), and a wrong or unsafe answer has real cost.
Teams whose releases keep breaking
You need a regression suite and a CI gate a release manager can actually trust.
Teams drowning in manual work
You have an intake, triage, or routing workflow a well-built automation could run — safely.
#How this documentation is organized
- What I build — the capabilities, documented: what each is, what you get, and how it works.
- The eval method — the philosophy and mechanics of proving AI features (with animated deep-dive guides).
- Working together — how engagements run, how your data is handled, and how pricing works.
- Reference — FAQ, a plain-English glossary, and an index of every proof artifact.