Designing for trust
How interfaces earn — and lose — trust at scale, starting with AI that nobody notices.
Case studies
Accessibility results, uninvited
Auto-enabling accessibility scans on ordinary test runs puts a11y results in front of teams who never asked for them — value realization and lead generation in one move. The design problem: presenting an uninvited, deliberately lightweight scan honestly, inside a dashboard built for a different job.
Trusting a model with your test suite
Teams with hour-long test suites don't need faster machines — they need to run fewer, smarter tests. I'm designing BrowserStack's exploration of predictive test selection and orchestration: the design problem isn't the ML, it's persuading an engineer to let a confidence curve decide what doesn't run before their release ships.
Developers can be nudged
BrowserStack's test dashboards are at their best when a team integrates the SDK — and a large share of teams never did, missing most of what the product could do for them. I designed the nudge system that closed the awareness gap: behavioral choice architecture applied to an audience that famously hates being marketed to.
AI agents as first-class citizens
BrowserStack's test platform had capable AI agents that almost nobody used. I led the design initiative that reframed this as an experience problem — invisible AI is not adopted AI — and shipped the awareness system that changed it, then watched the numbers decay and got to work on why.