
Mixed-methods researcher — qual leaning — studying cognitive behaviors for emerging tech, with roots in Human-Computer Interaction and Computer Engineering.
Your customers are telling your sales team exactly what they need — but that intelligence never reached product or research. I built a RAG system that turned thousands of scattered sales conversations into a queryable research layer — grounded, sourced, and informing a product roadmap within two months.
No single team owned the end-to-end economic experience. My research gave four siloed orgs a shared framework for value, ownership, and interoperability — and it became Meta's Metaverse monetization strategy. $3M TPV in year one.
In 2020, I watched a perfectly good automation fail. The feature was powerful — but felt opaque and irreversible. Six years later, every AI product team I talk to is living some version of this story: a capable system, a skeptical user, and adoption numbers nobody can explain.
A mapping of quantitative and qualitative measurements for AI products, built on the bones of Google's HEART — borrowed, with affection and mild guilt ❤️.
AI products don't just get informed by people — they learn from them. When the development process changes, every role around it changes, research included. The role doesn't shrink. It becomes more integral than before.
Not a guide to UX methods — a story about shifting a team's decision-making culture through research. From "Let's do ABC, does that work?" to "XYZ is the problem — how do we solve it?" Sixteen shadowing sessions. One team. A complete change in how product decisions got made.
This one's invite-only — not because I'm precious about it, but because NDAs are real. Got the code? Great. Want in? Ping me.
I promise it's worth it 🙈