Mimir

Mimir is a product research workspace that ingests interviews, surveys, tickets and analytics, then surfaces ranked themes, source-cited answers, a prioritized backlog and draft PRDs that improve as more sources are added.

Mimir is a workspace for product teams that have more customer evidence than they have time to read. Interview transcripts, survey exports, support tickets, analytics screenshots, Slack threads and meeting notes usually sit in separate tools, and the patterns across them go unnoticed. Mimir ingests those sources and builds a running model of the product, its users and its market, then uses that model to surface themes, answer questions and propose next steps. In practice, a user pastes, uploads or connects material such as text files, CSVs, PDFs, screenshots, Slack and URLs. Mimir groups what it finds into themes ranked by severity and frequency, with the original quotes shown inline so the reasoning can be checked. A chat interface lets people ask questions like which churn signal is strongest and get answers that cite the specific interviews or survey responses behind them. A prioritized backlog sorts recommendations by impact and effort, and the tool can draft PRDs, briefs and emails that refer to the actual research instead of a generic template. The homepage stresses that the workspace improves as sources accumulate. Early on, with only a few inputs, output is limited to basic themes. With a dozen sources it starts cross-referencing and pointing out blind spots, and with a few dozen it produces recommendations that weigh conflicting evidence, for example reconciling interview feedback with survey data. The site also claims a high share of outputs are traceable back to their source material, which matters for anyone who has to defend a roadmap choice in a review. The audience is product managers, founders and researchers who synthesize qualitative and quantitative input regularly. Compared with general chat assistants, it keeps persistent context about the product. Compared with dedicated research repositories or note tools, it leans toward producing decisions and documents rather than storing and tagging material. Teams with little feedback data will see less benefit, since its value grows with the volume of sources.