How to build an AI-driven User Research Repository

Leverage AI to make research smarter, faster, and to build a more user-centered product team

AI-driven research repo is not a dream anymore.

Imagine a world where every user insight is just a click away — no more digging through countless tools, endless Slack threads, or scattered notes.

How much time and effort would that save?

Think about the hours we’ve all spent searching for user data across Amplitude, Metabase, Notion — or even notebooks, random spreadsheets, stickies in Miro, comments in Figma, Slack threads, Google Drive reports, and Slide decks.

Nightmare.

Data, data everywhere.

While these tools help us document and communicate insights, having so many scattered platforms often distracts us from focusing on the right user insights. .

A report from UserInterview shows how different roles are impacted by this scattered approach to user feedback. For example, Researchers often rely heavily on data science/internal analytics, with around 70% of them using this method, while Designers and PMs often collect insights from customer support teams and through ad hoc…