I design interfaces for systems people can't see inside—energy automation, predictive analytics, AI agents. My work centers on a fundamental problem: how do you build trust when users can't understand the underlying complexity?

At Hager Group, I designed energy management systems that balanced automation value with user control. The core challenge wasn't UI polish—it was solving the trust paradox: users needed verification capabilities to trust simplified recommendations, but showing all data caused decision paralysis. My solution: progressive disclosure architecture that defaults to outcomes while enabling on-demand verification.

This taught me that great AI products require three elements: making opaque predictions interpretable (Legibility), ensuring users can act on information before it's obsolete (Timing), and providing control without undermining automation (Override). I'm looking to apply these principles to AI products where the stakes are higher.

Background: Product design for B2B2C systems, editorial design, brand strategy. I prototype rapidly, push back on feature creep, and use business constraints as design inputs rather than limitations.

Currently exploring AI-driven design workflows and seeking senior product design roles at AI companies.