nowfound

AI · August 21, 2026

Feedback Intelligence Jira Automation

Turn customer conversations into actionable product feedback

What it does

Feedback gets scattered across interviews, meetings, transcripts and spreadsheets. The context behind it often gets lost before it becomes a product decision. Feedback Intelligence turns conversations into structured feedback, preserving the source, user signal and domain context. Review and enrich it, create a draft ticket, and push approved feedback to Jira. Built for domain-heavy B2B products where the “why” behind a request matters as much as the request itself.

Extract actionable product feedback from meeting transcripts and turn approved items into Jira tickets.

Feedback Intelligence reads your meeting transcripts, extracts every piece of product feedback, and turns the approved ones into Jira tickets — so nothing said in the room gets lost. Paste text, import a file, or upload a recording — the AI transcribes it for you. Every item is shown in full. Approve, reject, or flag it as a duplicate. Approved items become tickets in your own workspace, tracked from here. Connect your own Jira workspace. Tokens are stored encrypted and only ever used by your server. Every extracted item is shown in full — nothing hidden behind a summary. Store a transcript and run the analysis whenever you're ready — no re-entering. Its a nicely built product via…from feedback-intelligence-prod.vercel.app

Does the same job

all alternatives →

More ai this month

the category →
  • I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.

    AI · 17d ago · simedw.com

  • Astute585

    Automate your B2B brand going viral, with new media creators

    AI · 18d ago · company-app.joinastute.com

  • Grok Bot547

    AI teammates that you can give real work to

    AI · 25d ago · x.ai

  • Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…

    AI · 26d ago · cactuscompute.com

  • Make your software self-driving

    AI · 30d ago · coldtea.ai

  • Soloop472

    Approval-first Agent OS for solo founders

    AI · 30d ago · soloop.io

Launched alongside, August 2026

the whole month →
  • TL

    Life & fun · 10d ago · louisabraham.github.io

  • Hey Noah641

    A proactive AI executive assistant for founders

    AI · Aug 2026 · heynoah.io

  • Let agents source clips from terabytes of your local video

    Work · 18d ago · clipto.com

  • SA

    Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…

    Life & fun · Aug 2026 · toneyalexander.github.io

  • AdAnt AI608

    Claude for viral, high-converting social ads

    AI · Aug 2026 · adant.ai

  • I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.

    AI · 17d ago · simedw.com