I see what AI makes possible
— then I build it.
Product operator and AI systems builder. I turn broken workflows into production systems, ship them, and study what I learned to find the shortest path next time.
Building finance dashboards when nobody writes the spec.
A one-sentence ask, no shared definitions. The PRD got built backwards: explore the data first with AI, settle the rules together, write the spec against the working prototype.
Building five CS skills for a team that didn't have a standard.
I'm not a CSM. The skills had to bake in pattern-matching I don't have. The way through was to play the meta-skill: substitute hard for missing CSM bandwidth, ship a v0 the team could push back on, and design an intake step that lets their judgment fill in on every run.
Building an AI operations system.
A 50-tool MCP server, automated daily digests, and a ticket workflow — built from frustration, refined through a deliberate reflection loop.
Knowledge architecture, from scratch.
120+ docs, a living decision log, enablement summaries, and a self-updating registry — designed so the team always knows what to do next.
Communicating model changes.
Translating statistical model updates into language that builds client trust instead of triggering escalations. 37 releases shipped without a crisis.
→Building
- AI-native ops infrastructure
- Portfolio site (this one)
- Claude Code custom skills
◊Reading
- Eval design patterns
- Agent architecture
- Measurement methodology
~Thinking about
- Where AI judgment fails
- Token economy workflows
- The reflection loop as practice
Three embedded AI demos that let you experience how I approach workflow decomposition, model communication, and portfolio Q&A.