The featured case study below is the live multicloud rebuild I led at CloudHealth · VMware · Broadcom. Active platform work at AWS — agent surfaces, an AI-Ready Design System, autonomous workflows, and design-org AI initiatives — runs alongside it, and lands here as it reaches customers.
One project walked end-to-end here: research, decisions, redesign, and the data on what shipped.
Active platform work at AWS. Themes below; full case studies follow as the work ships to customers.
A design system built for an agentic desktop. Less about the components themselves and more about the layer between a human composing a Skill and the model interpreting it. The protocol is standardized; the experience inside it isn't — and that's where AI either quietly works or quietly breaks. The system makes the inside legible to both.
Design lead on the program that runs every step of an enterprise sales motion — and the meeting workflows that bookend each call with autonomous prep, intel, and follow-up. The pattern: the AI's recommendation is the first read, not the footnote, and the human's job is approval, not archaeology.
An AI co-pilot for the design lifecycle — distills multi-stakeholder research into actionable backlogs, generates wireframes with validation loops, and keeps the design org from re-discovering the same insights every quarter. Pilot feedback synthesized in minutes. Insights routed into Asana as tagged roadmap items. Figma components auto-generated for stakeholder comment — one continuous loop instead of three handoffs between research, design, and engineering.
Org-wide work bringing AI tooling to the broader design organization — a
spec-driven workflow that mirrors Kiro's
requirements.md → design.md → tasks.md,
a shared component library every designer can ship from, and the prototypes
that turn into shipped product faster. Less "designers experimenting with AI",
more "designers shipping AI-native products at the same pace as the rest of
the company."
Detailed case studies follow as the work reaches customers.
