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Nugget AI
Turn customer interviews into your product roadmap
202 followers
Turn customer interviews into your product roadmap
202 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.
Nugget AI
Product Hunt
Nugget AI
@curiouskitty Good question. The short version: opportunity scoring uses frequency, severity, segment, and recency as inputs — but those are weighted against the goals and strategy you define on the Priorities page. That's the main tuning surface.
This is also how we handle the loud-customer problem. Without strategic context, scoring just reflects volume. Because Nugget anchors to your priorities, a critical pain point from a key segment can outrank a noisy-but-irrelevant request. The PM's judgment about what matters is baked into the system, not overridden by it.
Indie iOS founder here, doing a lot of customer discovery ahead of an App Store launch, so the "interviews die in transcripts no one reads" line hits home. The MCP server is the smart unlock; an agent citing real users instead of hallucinating them is the whole game.
Answering your q: the gap I'd flag is upstream of synthesis: interview quality varies wildly, and a confident-but-shallow interview produces clean-looking Nuggets that are actually just the customer agreeing with my leading questions. Does Nugget do anything to flag low-signal interviews (interviewer talked 70% of the time, all closed questions, sentiment too uniformly positive), or does it treat every transcript as equally trustworthy evidence?
Nugget AI
Our Stories
Nugget AI
Our Stories
@brodie_sv thank you - i missed the section integrations which is very clear on your home page! love it! good luck!
Our Stories
@brodie_sv i think the tools you integrate are more than enough, but some companies may stick to less and more traditional. I would vote Jira - i think it solely depends on what tools companies use for their PM teams.
I’ve personally been in situations where we did multiple customer calls, captured tons of feedback, and still ended up writing PRDs mostly from memory because nobody had time to go back through long transcripts. A lot of valuable insights just get lost in docs and scattered notes.
Really like how Nugget focuses on connecting customer conversations directly to product decisions instead of letting that context disappear. The MCP integration is especially interesting because AI tools become much more useful when they can reference actual user feedback instead of assumptions.
Congrats on the launch, Brodie!
Nugget AI
hi brodie, the right pain to solve! how do you weight one confident interview against five lukewarm ones? and what stops the AI from inventing patterns from a thin sample? very cool - congrats and good luck!
Nugget AI
@hiyamojo Really good pushback - both of those are live design problems.
Honest answer on weighting: we don't want the AI making that call. It should surface the signal strength and the context (who said it, when, with what level of conviction) and let the PM decide. That judgment call is the job.
On invented patterns: strict attribution is the constraint. No pattern without a source, and thin sample sizes are surfaced explicitly. The goal is for AI to be a rigorous research assistant, not an opinionated analyst.
Makers Page
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