Product Loop – Automated AI customer interviews

Hacker News

Hi HN — I built Product Loop because I found customer interviews slow, inconsistent, and hard to keep up with. Scheduling calls, chasing users, and taking notes was eating hours every week. Product Loop runs automated voice interviews with your users, asks follow-up questions, and summarizes the insights so you know what to build next. Here’s how it works technically: Uses an AI voice agent for natural conversation Runs in the browser (no app install) Extracts themes, pain points, and feature requests Generates a structured interview summary You can trigger interviews via link or email Would love feedback from this community — especially on: What’s unclear in the UX What feels unnecessary Whether the AI interviewer feels natural enough Any technical improvements you'd recommend Check it out: https://productloop.io Happy to answer anything.

Visit product View on Hacker NewsNov 25, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: agent, user, email · Missing: mac, agents, macos

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: users, calls · Missing: plus, platform, intuitive

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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