About

I write about building reliable systems—lessons from two decades in product and operations roles where reliability mattered more than novelty.

Experience

Amazon — Built and scaled Amazon's product-safety systems used across global retail. Delivered the ML models and operating architecture behind 40M+ weekly interactions in 54 languages. Presented predictive safety controls to the Board and named inventor on U.S. Patent 10,223,353 (Amazon's Choice).

StockX — Delivered the company's first multi-year fulfillment roadmap and launched Express Ship—a profitable business line that cut delivery times from 9 to 3 days and reduced support costs by 75%. Rebuilt the product and engineering operating model to accelerate experimentation and reliability.

Stripe — Led global onboarding compliance and merchant-risk systems—handling hundreds of billions in payments for millions of businesses. Built an LLM-powered policy-evaluation engine that replaced hundreds of brittle keyword rules, reducing manual reviews by 80% and delivering Stripe's multi-year compliance automation roadmap.

I've spent much of my career designing systems that make organizations more reliable—whether for global marketplaces, supply chains, or financial infrastructure.

Now

Building an AI assistant for executives—focused on reliability and decision quality, not demos that impress but systems that endure.
Writing about what I learn: the tradeoffs, the failures, and the patterns that actually work.

Topics

AI systems — Practical reliability, not research theater. How to build AI leaders can trust.
Leadership frameworks — Decision-making systems that scale. Lessons from leading teams through hypergrowth.
Software craftsmanship — Architecture that lasts. The unglamorous work that makes systems dependable.

Why This Site Exists

Most writing about AI and leadership is either academic or promotional. This site is neither.
I write about what I'm actually building and learning—no pitch decks, no hype, just clear thinking about hard problems.