Nugget AI

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?

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?

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!

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?

把用户反馈转化为路线图决策是独立开发中最难的部分之一——你接收到零散的信息，必须自己找出规律。它支持 App Store 评论，还是主要处理结构化访谈数据？