Internal platform search
Role: lead designer
Situation
Atheneum's internal platform required significant manual effort to locate relevant experts. The existing search function often returned inaccurate results, relying heavily on basic keyword matching.
Task
Our goal was to improve the platform’s search functionality, making it more intuitive and enabling internal teams to find the right experts more efficiently, based on context, not just keywords.
Project success
✅ AI-enhanced search results provided more relevant matches by understanding context.
✅ Streamlined interface reduced time spent searching.
✅ Fewer searches needed to identify suitable experts for project.
Action
I conducted user interviews with internal teams to uncover key pain points and search behaviours, collaborated with data science to integrate AI for contextual understanding, built a coded prototype using real data, and iterated based on feedback to improve usability and flexibility.
Result
The improved search interface delivered significantly better results. Internal teams were able to find and add experts to projects with fewer searches, thanks to AI-driven suggestions and more flexible filters.
Outcome
The revised search experience was rolled out in phases and continues to evolve based on user feedback. Overall, it made a tangible difference to the day-to-day work of internal teams by reducing effort and increasing confidence in search results.
Personal highlights
Helping simplify daily workflows for internal teams.
Collaborating closely with data science to improve relevance through AI.
Gaining a deeper understanding of search systems and the nuances involved in designing for complex, flexible queries.
Note: this case study follows the STAR framework and is intentionally concise to provide a brief overview of the project. If you'd like to learn more, please feel free to reach out!