Nugget AI

Turn customer interviews into your product roadmap

SaaS

Artificial Intelligence

Alpha

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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?

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!

Can you walk through how “Smart Prioritization / opportunity scoring” works in practice—what inputs it uses (frequency, severity, segment, recency, goals), how it avoids overweighting loud customers, and what knobs a PM can tune to match their strategy?

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!

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.

Love the MCP server → Cursor setup.

Does it auto-pull context from all past interviews, or do you select specific ones for each session?

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!

I really like nugget AI Does it integrate with systems like Jira or any type of kanban board? What type of artifacts does it help the PMs produce?

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.

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?