SetSmart

TrustMRR

SetSmart is a SaaS platform that uses AI to automate lead qualification, follow-ups, and appointment booking through Instagram DMs and WhatsApp. It helps coaches, agencies, and online businesses convert more inbound leads into booked sales calls while reducing the need for manual setters.

Visit product View on TrustMRRMay 26, 2025whop

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Actual performance

Did not reach leaderboard

Traction signals

Domain Rating38

Launch Intel predictions

Analyze your own launch →

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

68%68% 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.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, 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: platform, calls · Missing: plus, intuitive, reviews

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

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

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

20%20% predicted probability of success on BetaList, 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

17%17% predicted probability of success on Hacker News, based on ML models trained on real launch data.

Incorrect prediction on native model

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