Veeva QuickVault Enterprise Search
Enhancing document discovery and AI-powered insights through a semantic search experience
Role
UX Designer
(Contract)
Timeline
Oct. 2025 - Dec. 2025
(8 weeks)
Team
3 Product Designers
1 Project Manager
5 Developers
Skills
Design Systems
Systems Design
User Research
Tools
Figma
Google Gemini
Project Overview
As a UX Designer at Veeva Systems via contract by CodeLab, I led the end-to-end design of an enterprise semantic search tool utilizing RAG technology. The key goal was to transform complex document discovery into a streamlined experience to empower MedTech teams to make rapid, confident decisions with deep AI-generated insights.
Contributions
While the specifics of this project are restricted under NDA, my contributions included:
Pivoting the product vision from a conversational chatbot to a semantic search interface after identifying technical and timeline constraints. This strategic shift allowed the team to prioritize a document discovery tool for the immediate POC while documenting the conversational AI experience as a future roadmap item.
Architecting the search flow and visual hierarchy based on a competitive analysis of leading enterprise search tools. I focused on improving information density and scannability by replacing a stacked icon navigation with tabbed nagivation for organized metadata and AI summaries, allowing users to rapidly validate relevance at a glance.
Delivering high-fidelity designs for the search interface while contributing new, scalable components to the existing design system. This ensured the new features were visually cohesive and provided a documented foundation for seamless integration into the broader QuickVault product ecosystem.
Validating the proof-of-concept through a functional demo developed in collaboration with engineering. Presenting the final designs to stakeholders demonstrated significant reductions in information retrieval time, confirming the tool’s effectiveness and securing stakeholder buy-in.
Reflection
Looking back on the experience
It was a pleasure working with the Veeva QuickVault team based in Ontario, alongside my teammates from CodeLab. With this project starting out my final Fall quarter as an undergraduate at UC Davis, it's shown me yet another side of the industry I've always had a curiosity in designing for. I hit an initial learning curve in understanding the technical-heavy aspects of Retrieval-Augmented Generation, but it was ever so more rewarding after tailoring my designs to optimize for these emerging technologies. Special shoutouts to Jason MacDonald and Mike Petro for all the guidance and advice throughout the project!
Takeaways
Lessons and learnings
Align design scope with evolving project constraints. Confirming needs is an ongoing dialogue, not always a one-time handoff. Pivoting the vision to meet a tight timeline taught me the value of prioritizing a feasible MVP over an initial concept that no longer fits technical feasibility.
Balance design system adherence with purposeful innovation. Operating within an enterprise system requires standardized visual cohesion, but it shouldn't discourage improvements when opportunities arise. Pitching new, scalable components never hurts when they meaningfully improve information density.
Humanize technical complexity through cross-functional collaboration. Designing for RAG-powered tools required constant communication with developers to demystify AI retrieval. By providing relevant metadata within a clean interface, I built user trust without overwhelming them with underlying technical complexity.