Every product team has the same problem: the signal about what to build next is scattered across interviews, support tickets, Slack threads, and survey exports. The data is there. But reading, tagging, and synthesizing it takes days — and by the time you're done, you've forgotten what the first ten interviews said.
Mimir is an AI-native product management workspace that does this for you. Not by organizing your data for you to analyze later, but by actually analyzing it and telling you what it found.
Here's what happens when you use it
You paste customer feedback — an interview transcript, a batch of support tickets, a Slack conversation — directly into Mimir. Or you upload a file: CSV, PDF, text, even a screenshot of app reviews.
Within about 15 seconds for a single source, Mimir reads your data and extracts discrete signals: pain points, feature requests, workflow observations, metrics, and notable quotes. It clusters these into themes ranked by severity and frequency, with every theme linked back to the exact source evidence that supports it. This isn't keyword matching — Mimir understands meaning, so it groups related signals even when people describe the same problem in completely different words.
From those themes, Mimir generates prioritized recommendations for what to build next. Each recommendation is grounded in specific evidence — you can trace any suggestion back to the customer quotes and data points that support it. For a batch of 15-20 sources, the full pipeline takes about a minute and a half.
But analysis is just the starting point.
A workspace, not a report
Mimir opens into a three-panel workspace: a chat in the center, your insights and recommendations in tabs on the right. This is where the product becomes more than a one-shot analysis tool.
Ask questions about your data. "What are the top complaints from enterprise users?" or "Do any customers mention our onboarding flow?" Mimir answers using your actual sources — not generic advice, not hallucinated summaries. Every claim traces back to evidence.
Pressure-test decisions. You're considering building a new dashboard. Ask Mimir whether any customers have mentioned reporting problems, how severe the signal is, and what the evidence says about priorities. Get a second opinion grounded in data instead of gut feel.
Refine recommendations. View a recommendation and tell Mimir to adjust it — change the scope, shift the priority, or rethink the approach. Mimir updates the rationale in real time, keeping the evidence chain intact.
Generate artifacts. Need to share your findings? Ask Mimir to write an email summarizing the top insights for your team, draft a product brief for a specific recommendation, or produce a PRD. These aren't generic templates — they're grounded in the analysis Mimir already did, with specific evidence woven in.
Context that compounds
Most AI tools forget everything between sessions. Mimir remembers.
Every source you upload and every conversation you have feeds into a living context system. Mimir builds and maintains a structured understanding of your product across seven categories: company profile, competitive landscape, user segments, goals and constraints, product state, terminology, and key metrics. Each piece of context carries a confidence score based on how many independent sources support it.
This means the tenth time you use Mimir is meaningfully better than the first. It already knows your product, your users, your competitive landscape, and your strategic priorities. It doesn't need you to re-explain the basics every session. When new feedback arrives, Mimir interprets it through the lens of everything it already knows.
Who is Mimir for?
Mimir is built for the people who own product decisions:
- Founders who need to move fast and can't spend days synthesizing feedback manually. Paste a batch of customer interviews and have a prioritized plan before lunch.
- Product managers who want to ground their roadmap in real customer evidence, not gut feel. Use Mimir's insights to build the case for what to build next.
- Engineering leads who want clarity on why something is being built. Every recommendation links back to evidence, so the rationale is inspectable, not just "PM said so."
If you've ever stared at a spreadsheet of customer feedback wondering "what's the pattern here?" — that's the problem Mimir solves.
How is Mimir different?
vs. Manual feedback analysis
Manually reading, tagging, and synthesizing customer feedback takes hours or days. Mimir does it in under a minute for a single source and about 90 seconds for larger datasets. More importantly, it finds patterns across sources that are nearly impossible to spot when reading one interview at a time — and it never forgets what interview three said by the time it's processing interview eighteen.
vs. Traditional PM tools
Tools like Productboard and Canny help you organize feedback and manage roadmaps. But they still require you to do the analysis — reading, tagging, prioritizing manually. Mimir does the synthesis automatically and goes further by generating actionable recommendations. And because it's a workspace with chat, you can keep working with the analysis instead of exporting it somewhere else.
vs. Research tools
Tools like Dovetail are excellent for organizing qualitative research — tagging, highlighting, and searching across interviews. Mimir starts where those tools stop. Instead of organizing your data for you to analyze later, Mimir analyzes it and tells you what it found. Then it lets you have a conversation about the findings.
vs. Generic AI chat
You could paste feedback into ChatGPT or Claude and ask "what should I build?" But generic AI doesn't have the structured pipeline to extract entities, cluster themes, or cross-reference across dozens of sources. It doesn't remember what you uploaded last week. And it can't trace its recommendations back to specific customer quotes. Mimir is purpose-built for this workflow — the analysis is structured, the evidence is attributed, and the context persists.
Integrations and inputs
Mimir works with whatever you already have:
- Paste text directly — interview transcripts, feedback notes, survey responses, anything
- Upload files — CSV exports, PDFs, text files, or screenshots (PNG, JPG, WebP) via drag-and-drop
- Slack — capture team conversations using the
/mimircommand,@Mimirmentions in channels and threads, or the message shortcut. Thread content is formatted with participant names, timestamps, and reactions preserved
No complex setup or data migration required. If you can paste it or upload it, Mimir can analyze it.
How fast is it?
Mimir is built for speed. The analysis pipeline runs extraction, synthesis, and recommendation generation as a continuous process:
- Single source: insights and recommendations in about 15-20 seconds
- 15-20 sources: full analysis in about 90 seconds
- Chat: responses stream in real time, grounded in your full evidence base
You should have something useful to work with within a minute of uploading your first source.
Getting started
- Go to mimir.build
- Paste customer feedback directly into the landing page
- Sign in with Google
- Get your first insights and recommendations in under a minute
No credit card required. No setup. Paste and go.