Teams needed faster ways to find answers across large volumes of internal documents, manuals, and fragmented knowledge sources. Traditional search required users to know exact keywords, document names, or where information was stored — slowing productivity and increasing frustration. The opportunity was to improve knowledge discovery through conversational AI.

UX Designer responsible for experience design, conversation flows, prototyping, and usability validation in collaboration with product and engineering teams.

I designed a search and chat experience that allowed users to ask questions in natural language and receive relevant answers quickly, without needing to navigate multiple repositories.
Key experience principles included:

• Simple conversational entry point
• Clear answer summaries with source references
• Guided follow-up questions to refine results
• Reduced cognitive load compared with traditional search flows
• Trust-building through transparent information sources

The goal was to make enterprise knowledge access faster, easier, and more intuitive.

Internal users rarely search using exact document titles or system terminology. They search based on tasks, problems, or incomplete information.
By combining natural-language queries with guided question suggestions and structured refinement, the experience reduced search friction and helped users reach trustworthy answers faster.
This lowered the learning curve while improving confidence in enterprise knowledge discovery.

The solution improved speed and confidence in finding information, reducing time spent manually searching across multiple systems and documents.
It demonstrated how AI-assisted knowledge discovery could streamline productivity in document-heavy enterprise environments.

• Conversational AI UX
• Enterprise search experience design
• Knowledge management workflows
• Trust and transparency in AI interfaces
• Simplifying complex internal systemss