Open to interesting problems

Architecting decision intelligence
for AI-native enterprises.

Fourteen years shipping enterprise products. Two consecutive 0-to-1 enterprise AI platforms. Now operating the decision layer between LLMs and the enterprises that depend on them.

02About

Built like a product, run like an operating system.

Full background
Builder

I treat AI products as probabilistic systems, not feature trees.

Eval-driven roadmaps replace opinion-driven ones. Acceptance criteria become measurable; the “is this good enough yet?” question becomes the PM’s job.

  • AI-native SaaS, decision intelligence, AI/ML asset governance, LLM in production.
  • Two consecutive 0-to-1 platforms shipped. Concept to GA with end-to-end ownership.
  • Compound AI over single-model. The LLM is rarely the decider.
Operator

End-to-end ownership is a strength, not a constraint.

I ship live LLM products on the side. The patterns I learn there show up in enterprise work weeks later. Concept to GA across product, GTM, partner enablement, and analyst relations without dedicated PMM.

  • Anthropic, Gemini, Llama via Groq. ChromaDB, MLflow, Microsoft Fabric.
  • Write it down. If a decision isn’t in a doc, it didn’t happen.
  • Real authority on what the product becomes. Not just what ships.

04Recruiter Copilot

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Streams a Claude-generated fit analysis grounded in my résumé, philosophy, and project highlights. Four sections: fit score, top strengths, gaps, talking points.

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OutputFit Score · Top Strengths · Gaps · Ask About. Streamed and grounded in my public material only.