AdTech · Programmatic Advertising Platform · 2019–2026
I led design for a programmatic advertising platform where AI-driven media recommendations were going unused. As the sole product designer working with the ML team, I argued the algorithm wasn't the product — the buyer's confidence to act on it was. I designed the trust layer between media buyers and the recommendation engine: confidence scoring, reasoning surfaces, and override mechanisms designed as first-class interactions. Adoption moved from 15% to 63% in 90 days. Client is under NDA. The decisions, the design language, and the ML/UX contract that came out of this work all carry forward.
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I can walk you through the decisions, the visuals I cannot host publicly, and the outcomes — under mutual NDA on a 30-min call.
Book a 30-min callDesign Patterns Used in This Case
This project directly informed the development of two core patterns now used across multiple AI products:
- Confidence Score Patterns: All confidence visualization techniques used here — numeric, color, language, gauges — became the foundation of the Confidence Score Patterns library.
- Human-in-Loop Patterns: The override and feedback mechanisms became core examples of keeping humans in control when AI makes recommendations.
- ML Explainability Patterns: The reasoning surfaces and feature importance visualizations that buyers used to understand algorithm decisions directly informed the explainability patterns library.
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