AI-native product manager with data science rigour.
Shipped an AI tool that cut specialist cycle time 90%+. Built production AI that anchored an enterprise commercial engagement as lead technical. Built trust across four B2B stakeholders by making compliance a visible product feature.
Each one framed from a product-management point of view, with technical depth where it's earned. Tap any row to read the full case study.
A multi-agent pipeline that replaced panel recruitment for brand research — where the validation layer, not the generation, became the moat.
An AI tool that replaced a specialist-gated 3-week workflow with a 3-day one.
A B2B procurement prototype for cross-border construction materials — pivoted twelve times until it worked.
An open-source multi-agent pipeline that reads the job market daily, honestly, and at the cost of a coffee.
AI fluency is necessary but not sufficient. These are the product-management moves that decide whether an AI build ships, sells, and sticks.
Each one comes from having shipped an AI product and watched it meet the real world. Each one links to the case study where it showed up.
Every role has trained for the AI product seat. Data science gave me the rigour on outputs; delivery management gave me the discipline of shipping; current work put both together.