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Cora - Enterprise AI

Designing a guided path from
complex quality data to
actionable recommendations

I designed a guided enterprise workflow that helps quality teams select relevant data, review their inputs, and generate AI-supported audit focus area recommendations.

New Use case

Human + AI

Enterprise Desktop

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Project Details

Role: Lead XD Designer

Year: 2026

Team: Product, Developers, SMEs

Platform: Desktop responsive web application

Focus: AI-assisted workflows, complex data selection, and audit recommendations

My Contribution: End-to-end UX, interaction design, prototyping, and design direction

The Journey

Turning a complex task into a guided 4-step experience.

Rather than exposing users to the complexity of Cora's underlying data and AI capabilities at once, I structured the experience around four clear decisions: choose the use case, select the data, review and confirm the inputs, then generate the analysis.

SEE FIGMA HERE

4 step process
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Designing within real data constraints

During implementation, technical constraints made the record-level data required for Audits, NCs, CAPAs, and Escalations unreliable. I simplified the experience by removing the detailed tables while preserving the core Individual Supplier workflow.

Simplifying Without Breaking the Flow

I removed the unsupported record-level tables and simplified data selection while preserving the core Individual Supplier journey.

Selection Step
My Collection Update

Improving the Experience Beyond the Use Case

While designing the Individual Supplier workflow, I identified an opportunity to improve My Collection, an existing shared Cora experience. I redesigned removal behavior to make individual actions immediately reversible with Undo, while keeping confirmation for clearing the entire collection.

Outcomes

What the work delivered:

The final experience turned a complex quality-data workflow into a guided path that Cora could reliably support. The design balanced user needs, technical constraints, and AI capabilities without losing the core goal: helping quality teams reach actionable audit focus area recommendations.

  • Introduced a clear, guided workflow for a new Cora use case

  • Simplified data selection when record-level data could not be reliably supported

  • Preserved review and confirmation before AI generation

  • Improved My Collection with safer, more intentional removal patterns

  • Established reusable interaction patterns for future Cora workflows

I wasn't designing AI for the sake of AI. I was designing a clear path through complex quality data, so people could understand what they were giving Cora and trust what came back.

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