Designing AI for professional skeptics

Explainability patterns and collaborative workflows that make complex systems legible, reliable, and trusted in high‑stakes environments.

  1. Evolving Search interface showing the unified search bar

    Evolving Search for Intelligence

    Primer AI    From Boolean syntax to AI-interpreted intent for intelligence analysts.2024–25Case StudyAI AdoptionSearch UX
  2. RAG-V's verification pattern inside Primer's agentic search interface

    Making AI Verifiable

    Primer AI    Claim-level verification for every AI-generated statement.2024Case StudyVerificationScale
  3. A refined product dashboard mock lifted off an agent coding session building it

    Designing Against the Model

    Primer AI    Three questions about AI-native design, tested before trusted.2026–OngoingProcessAgentic CodingAI-Native
  4. UXAI framework cards

    Bridging ML Complexity and Trust

    UXAI    An explainable AI design framework co-founded at UC Berkeley.2020FrameworkExplainability
  5. Eightfold email modal with multiple follow-ups and scheduled send

    Enabling Efficient Outreach

    Eightfold AI    Multiple follow-ups and scheduled send for recruiter outreach.2019Case StudyEnterprise UXWorkflow Design