Concipe
Evidence-backed product decisions for teams shipping with AI
Start new thread
David Jeremiah

Concipe - Turn customer feedback into specs for your coding agent

by
Concipe
•
84% of product teams worry their product won't succeed — yet feedback sits scattered across 15 tools. Concipe fixes that. Upload interviews and tickets, or connect Slack and Notion. AI extracts insights, ranks opportunities by evidence, and generates structured specs. Every recommendation traces to real user quotes. Connect via MCP and your coding agent pulls specs directly. Feedback to engineering-ready spec in under 10 minutes.

Add a comment

Replies

Best
David Jeremiah
Maker
📌
Hey Product Hunt 👋 I built Concipe because I kept seeing the same problem — product teams drowning in feedback from Slack, support tickets, and interviews, but still writing specs manually based on gut feel. Concipe closes that gap. It connects to your feedback sources, extracts insights automatically, and generates coding-agent-ready specs with every claim backed by real user quotes. Y Combinator's Spring 2026 RFS called for exactly this — a "Cursor for Product Management." Concipe is live and free to try. Would love your honest feedback. concipe.com
Victor N

How do you distinguish genuine feedback and prioritise?

David Jeremiah
Maker

Hey @viktorgems , great question. Two things: frequency and specificity. Feedback that shows up across multiple sources independently — say, the same complaint in a support ticket, an NPS response, and a Reddit post — gets weighted higher than a one-off. And specific feedback ("I can't export to CSV") ranks above vague feedback ("the UX feels off"). Concipe tracks both signal strength and source diversity before surfacing an opportunity, so you're not just seeing what's loudest, you're seeing what's most consistent.

Elia Yakin

How do you handle domain-specific language (ubiquitous language) used within the company?

David Jeremiah
Maker

Hey @elia_yakin , great question. Right now Concipe works with the language in your feedback as-is — it doesn't impose a taxonomy on top of it. So if your team calls it "workspace" instead of "project," that term carries through into the generated spec. Longer term, custom glossaries and domain-specific labeling are on the roadmap. What's your use case — are you working with a highly specialized domain?