A look at what I've shipped, and the ways I take on new work — fractional, advisory, or project-based.
Owned the product strategy for a conversational Q&A experience designed to answer real customer questions before purchase — not generic FAQs, but specific, contextual answers grounded in product data. The hardest part wasn't the AI; it was deciding, case by case, when an uncertain answer was worse than no answer at all. I worked closely with applied science and trust & safety to get that line right.
Shaped the product strategy for new generative AI shopping experiences on Alexa, at a point when LLM-powered shopping was still mostly a demo, not a product. My job was to turn genuinely impressive early capability into something dependable enough for customers to actually use — which meant a lot of unglamorous work on evaluation, edge cases, and knowing where to hold the line on launch readiness.
Partnered with global engineering and business teams to bring AWS technology and services into new markets. The technical work was the easy part — the real job was translating deep technical capability into something regional teams could adopt with confidence, across different regulatory environments, infrastructure realities, and customer expectations.
Format and duration flex to the engagement — these are starting points for a conversation, not fixed packages.
Embedded product leadership for a team shipping AI-driven features, without the overhead of a full-time hire.
A focused sprint to find out whether an AI idea is actually worth building — before anyone writes production code.
For founders and teams who know roughly what they want to build and need a sharper plan for how to build it, and whether to build it at all.
For teams who've shipped an AI feature and need an honest, outside read on whether it's actually good.