Gloria Murillo
Designing AI that earns
its users’ confidence
Senior UX designer for AI products in regulated, enterprise, and high-stakes domains. I design the transparency, verification, and safety mechanisms that let users form confidence in a system on their own terms.
Work
Projects across AI, enterprise, and trust-sensitive systems.
Sensory Sprout
An AI meal planning system for families managing sensory-sensitive eating. Designed layer by layer around the cost of getting it wrong: deterministic where failure is irreversible, AI where judgment is recoverable.
RefWorks
Earned voluntary migration from expert researchers who had refused to move for years. Trust design at enterprise scale.
StoryJam
Reframed a showcase brief as a participation infrastructure problem. Protected independent thinking before group visibility — and the behavioral change followed.
Anne-bot
AI prototyping as diagnostic instrument — recommended pausing before automation scaled the wrong thing.
Mezzo
Research, design, prototype, ship in six days, solo. A private volume-awareness tool built on what a system structurally cannot do.
Principles
How I think
Principles that have held up in high-stakes, ambiguous work.
Trust is earned, not installed
Trust belongs to the user. My job is to design the conditions that let it form — transparency, verification, safety boundaries that make each interaction evidence, not a claim.
Diagnose the real problem
Before designing anything, identify what's actually blocking progress. The visible problem is rarely the real one. In AI work, the request is often “build this feature” when the real answer is “pause and establish shared criteria first.”
AI needs judgment, not just enthusiasm
Separate layers by failure cost. Match each capability to the right technology. A missed allergen is not the same as a suboptimal suggestion. Know which failure modes are recoverable — and which are not.
Restraint is a strategy
Sometimes the most valuable deliverable is the recommendation to stop. “What we chose not to build” is a first-class decision — especially in AI, where scaling the wrong thing compounds faster than fixing it.
About
What I bring
Strategy, not just delivery.
I work best where product direction is still forming — when teams need clarity, not just execution. I'm usually most useful in the early stages: framing the problem, surfacing the real constraint, and helping the team decide what matters most.
My background in UX keeps me close to users and interaction quality. My product work taught me to step upstream — to shape the problem before building the solution, and to name what not to build before committing to what to build.
I've designed AI systems from research through evaluation, shipped enterprise tools to thousands of users, and earned the confidence of experts who had every reason to resist. The thread across all of it: AI products hold up when someone does the hard thinking about where human judgment still needs to live.
I work across disciplines with enough fluency to connect design, product, and engineering — and enough judgment to know where alignment, tradeoffs, or restraint matter most.
Let's talk
Let's build AI that holds up
when the stakes are real.
Product strategy, AI systems, and trust-sensitive design.
I'm open to roles where I can help teams frame ambiguous problems, make better decisions, and build systems that hold up in practice.