Launched this week
Nugget AI
Turn customer interviews into your product roadmap
200 followers
Turn customer interviews into your product roadmap
200 followers
Nugget AI turns customer interviews into product evidence. Record or upload calls → AI extracts pain points and feature requests → synthesis surfaces themes → auto-generated PRDs with real customer quotes → dev-ready handoff to Linear & GitHub. NEW: MCP server. Connect Claude, ChatGPT, Cursor, or Codex — your AI agent can search every interview and draft specs grounded in real evidence. No more copy-pasting. Half the price of Dovetail.
Product Hunt Wrapped 2025
Interview graveyard vibes here. Notes in Notion, gut in the PRD. If it really pulls themes and spits PRDs with quotes, that's useful. The MCP angle is neat grounding chatgpt on real calls. Keen to try on my next batch. Solo PM here, time savers matter.
Nugget AI
interesting... isn't this similar to ReadAI/Other notetakers?
Nugget AI
The MCP piece is a smart wedge because the evidence can travel into the actual spec-writing environment instead of dying in a research repo.
One thing I’d want in every generated PRD is an evidence receipt: strongest quotes, counterexamples, last-heard date, interview-quality score, and which asks were intentionally left out. That last bit matters because product teams don’t just need “customers said X”; they need to see the judgment call behind why X became roadmap material and Y didn’t.
Nugget AI
@jim_jeffers Love this framing — especially 'interview-quality score.' That's the signal that usually gets lost when research moves from the PM's head into a doc. A quote from one frustrated power user and a pattern across 12 churned SMBs look identical in a PRD today.
The 'left out' field is the one I think would change team dynamics the most. It shifts the PRD from 'here's what customers want' to 'here's a defensible call' — and that's a different kind of accountability.
How are early users reacting to the product so far?
Nugget AI
@brodie_sv Great, All the best.
In EdTech the buyer and the user are different people - e.g. the teacher decides, the student uses it. Do interviews from both groups get synthesized together, or can you keep them separate and see where they diverge? Congrats on the launch!
Turning user feedback into roadmap decisions is one of the hardest parts of solo dev you get scattered signals and have to find the pattern yourself. Does it work with App Store reviews or mostly structured interview data?
The MCP server angle is what caught my attention. Most interview tools stop at transcription and tagging, but letting Claude/Cursor query the actual interview corpus directly feels like the right layer to integrate. I've had PRDs that felt data-driven but were really just the loudest customer's opinion repeated back — the synthesis step is where most tools fall apart. Curious if you've found a way to weight quotes by customer segment or recency, or if that's still manual?