Polygraf AI

AppSumo

Polygraf AI is highly valued for its seamless integration into workflows, accurate AI detection, and helpful comparison feature. However, some users have expressed concerns about token usage and limitations on certain features. Despite this, Polygraf AI remains a solid choice for those seeking reliable content analysis. With a 60-day money-back guarantee, it's definitely worth trying out.

Visit product View on AppSumoJun 13, 2025

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Actual performance

10reviews

Did not reach leaderboard

Traction signals

Rating2.3 / 5

Purchases139

Launch Intel predictions

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

61%61% 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: user · Missing: mac, agents, macos

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

46%46% predicted probability of success on AppSumo, 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.

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

34%34% predicted probability of success on Hacker News, 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

14%14% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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