Automate User Feedback

Hacker News

Hey HN, Baris here–cofounder of Pansophic, a tool that conducts user interviews and summarizes feedback using LLMs. My co-founder and I have found user feedback invaluable but hard to gather. Surveys lack depth and interviews are time-consuming to schedule, conduct, document, and summarize. To address this, we created Pansophic, which aims to combine the reach of surveys with the depth of interviews using AI agents. The agents interview your users, providing transcripts, summaries, and insights across all of the interviews as they happen. We’d love to get some feedback from the community, so please let us know what you think! If anyone wants to reach out directly, feel free to email me at the address in my profile.

Visit product View on Hacker NewsJul 21, 2023

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

90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

13%13% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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