Concipe
Evidence-backed product decisions for teams shipping with AI
44 followers
Evidence-backed product decisions for teams shipping with AI
44 followers
Concipe turns scattered feedback from Slack, Notion, Zendesk, and interviews into evidence-backed product decisions, with an Evidence Score for every recommendation and real user quotes behind every claim. Your coding agent pulls specs directly via MCP. From raw feedback to engineering-ready spec in under 10 minutes.
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How do you distinguish genuine feedback and prioritise?
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.
How do you handle domain-specific language (ubiquitous language) used within the company?
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?