Analyze Feedback with AI – Essense.io

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Hey everyone! Excited to show off something i've been working on recently. Essense.io Short pitch: Analyze user feedback with AI to generate actionable insights. Longer Pitch: Have you ever wanted to analyze your own product or that of a competitors' to figure out what the product is doing right? (and more importantly, what it's doing wrong?). With a few clicks you can figure out what users love and hate most about a product based on reviews posted to the Appstore, Playstore, Trustpilot, and more! You can also import your tickets from HubSpot or custom client transcripts to run the analysis against it. Notes: - Some folks may feel uncomfortable sharing private data (HubSpot tickets, Transcripts, etc...) with Open AI, totally get it. I'm working on fine tuning a llama model to extract insights so i don't have to send anything over but for an initial launch it seems to work well. For existing publicly available reviews (Appstore, Playstore, Trustpilot, etc...) there should be no worries there. - Adding G2 and Capterra soon, happy to take any suggestions for other sites to fetch reviews from. Fun fact: Best apps i've found to test for NEGATIVE reviews have been dating apps, boy does Tinder have some hate reviews. Happy to hear any and all feedback, positive or negative.

Visit product View on Hacker NewsMar 22, 2023

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Actual performance

4points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

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

96%96% 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 · Missing: supports, reddit linkedin, podcasting

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, existing · Missing: https docs, just released, lua

62%62% 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: reviews, soon, users · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% 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

17%17% 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.

Incorrect prediction on native model

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