I work at the intersection of Product Strategy × AI × Business Impact, from problem/thesis through prototyping, evaluation, guardrails, and production validation.
10+ years building and leading digital products across SaaS, platforms and regulated environments — now focused on AI-native product strategy and execution.
- AI Product Strategy — Turning emerging capabilities into clear product theses, choices, and business value.
- AI Prototyping & Validation — Testing the highest-risk assumptions early with evidence.
- AI Evals & Trust — Designing evaluation, guardrails, and human oversight into the product.
- Prototype → Production — Closing the gap between a promising demo and a product that works reliably.
AI Job Agent — Human-in-the-loop decision support
Product problem: Senior job searches create too much low-quality information and too little decision support.
Key product decision: Optimize for opportunity quality × candidate fit × evidence strength, max 3, no padding.
What it demonstrates: Product thesis through staged evaluation, deterministic guardrails, production use, and structured human feedback. My strongest prototype-to-production case.
From Document Management to Decision Management — AI-first B2B product strategy
Product problem: Document-centric B2B products risk treating AI as incremental instead of reconsidering the information-to-decision flow.
Key product decision: Reframe from improving document handling to supporting evidence-grounded decisions.
What it demonstrates: AI-first product discovery and strategic reframing for B2B SaaS — connecting customer value, evidence-grounded AI, evaluation, economics, uncertainty and human decision boundaries.
AI Lifestyle Companion — Strategy & prototype case
Product problem: Lifestyle products collect data but often fail to turn it into trusted personalized action.
Key product decision: Treat personalization, model economics, platform dependency, evaluation, and human safety boundaries as product-design questions from the start.
What it demonstrates: Greenfield AI product strategy and product-system design connecting customer value, personalization, product economics, evaluation design, governance, and platform risk.
Strategy → Discovery → Prototype → Evaluate → Production → Learn
I start with the user problem and product thesis, prototype the highest-risk assumptions, separate deterministic, probabilistic, and human decisions, and define evals and guardrails before increasing autonomy.
Modern AI-assisted development shortens the distance to evidence. The objective is faster, better-informed product decisions — not more code or more AI.