GenIAus — EY's GenAI auditing assistant, rebuilt from an ignored feature into a co-pilot auditors actually rely on.
A GenAI audit tool EY had already shipped — that auditors avoided because it gave them no hierarchy, no onboarding, no way to learn it.
Reframed it from a UI cleanup into a systems-level redesign — 8 decisions, shipped incrementally with a 10-developer team.
GenIAus was live, but nobody could figure out how to use it.
Eight states from the engineer-built beta, annotated during contextual analysis — every Nielsen heuristic violated, each violation compounding the next. Step through each frame to see the failure mode.
Every constraint stacked against a clean rebuild — this had to be a rescue, not a redo.
I re-framed GenIAus from a collection of AI features into a risk-aware human–AI decision system — built on reusable interaction patterns, contextual AI behaviors, and embedded transparency.
This wasn't a UI cleanup — the problems were structural, so the fix had to be too.
In regulated domains, users won't adopt tools they can't explain to a regulator. Every design decision had to answer the question: "Could an auditor justify this to their oversight board?"
A structured 5-day design sprint moved the work from analysis to validated structure — divergent before convergent, lo-fi before hi-fi, structure before surface. The carousel below cycles through the sprint's working materials; it advances on its own, but pause to study a frame any time.
5 days, from teardown to a clickable, testable prototype.
Contextual analysis + stakeholder interviews defined the core problem.
Flow variations on Miro, testing structure vs. flexibility.
Evaluated on time-to-value, scalability, buildability.
Lo-fi to validate structure, then hi-fi in Figma.
"How might we redesign GenIAus so that its AI capabilities are intuitively understood and seamlessly integrated into the auditor's existing workflow?"
After the sprint, I led usability testing with audit professionals — the actual end users — using a moderated think-aloud protocol focused on Document Intelligence and Prompt Scaffolding. Insights were synthesized through thematic analysis, empathy mapping, and affinity mapping.
Users couldn't act on what they couldn't see or understand.
"Find in Files" didn't say what it did. Renaming to "Document Intelligence" fixed comprehension immediately.
The empty chat state was a dead end. Structured entry points gave users a mental model before they typed a prompt.
80+ files in a flat list overwhelmed users. An audit-name dropdown first, then a filtered list, cut decision fatigue.
The AI's limitations had to be stated upfront — not legal cover, but what made people willing to engage.
Each redesigned feature was a deliberate response to a specific insight — the problem, the design decision, and the reasoning behind it.
8 problems, 8 targeted fixes — the problem, the decision, and why it works.
Respects the user's time — no one should scroll past 60 irrelevant files to find one document.
A UX solution to a prompt-engineering problem — the interface handles the context-switching instead of the user.
Bridges individual productivity and org standardization: users get a head start, the org gets consistency.
In a regulated audit workflow, accessibility and transparency turned out to be the same problem wearing different names — telling every auditor what the system can do, what state it is in, and what happens next.
WCAG used as a design constraint, not a compliance pass run against a finished interface.
Accessibility was a practical part of making GenIAus trustworthy, not a separate layer applied after the redesign. Auditors work where ambiguity is risk, so the decisions that made the AI legible were the same ones that made it operable: descriptive labels in place of vague feature names, structured Quickstart options and prompt suggestions to cut cognitive load, and visible processing states so nobody had to guess whether the system was working.
WCAG 2.2 organises accessible design around four principles — content should be perceivable, operable, understandable and robust. Those four describe what a cautious auditor needs from an AI system almost exactly, which is why the criteria worked as a design filter rather than a checklist. Every barrier I could name mapped to a pattern the product already needed.
Text, input labels, component boundaries and selected states were designed for readable contrast, and state was carried by label, icon and structure rather than colour. AA asks for 4.5:1 on body text, 3:1 on large text, and 3:1 on the visual information needed to identify a control and the state it is in.
Quickstart cards, chat history, document tables, persona selection and the prompt-library modal are all reachable by keyboard with a visible focus state. Focus cannot disappear behind the processing overlay or a modal layer — orientation survives the deepest part of the workflow, which is exactly where losing it costs the most.
“Find in Files” became a label that says what it does. Prompt suggestions, persona descriptions and upload constraints removed the assumption of prior AI knowledge. Interactive controls were sized and spaced against the 24×24 CSS pixel minimum WCAG 2.2 sets at AA, subject to its exceptions.
Full-screen processing feedback, progress indicators and completion toasts made AI activity visible. The implementation note that travels with them: these need to be exposed as programmatically determinable status messages, so a screen-reader user gets the same update without focus having to move to receive it.
Treating WCAG as a constraint rather than a checklist meant accessibility reinforced the thing the product was already trying to earn — trust, control, and an equal footing for every auditor using it.
Redesigning a live product with 8 developers required a different kind of collaboration. Here's what worked.
Findings framed as shared problems got buy-in; prescriptions got resistance.
These patterns became EY's broader AI product design standards.
Transparency isn't a feature in regulated AI — it's the foundation everything else stands on.
More upfront co-design with auditors. Workflow nuances only surface when you watch someone do the real work over days, not hours.
Earlier investment in a design system. Defining patterns as I went cost consistency I'd have kept with an upfront design-system sprint.
Transparency is structural, not optional. In regulated domains, every decision has to survive a regulator's questions.
Context retention improves efficiency. History, file persistence, and persona memory aren't features — they're respect for the user's time.
The best AI interface is invisible. When GenIAus works, auditors think about the audit, not the interface — the trust promised up top, delivered.
I'm open to senior product design roles, advisory work, and selective collaborations. Whether you have a defined brief or a fuzzy problem space, let's talk it through.
damleaalvee@gmail.com