Yapper

TrustMRR

Yapper is an AI phone receptionist for trades businesses — plumbers, HVAC, electricians, roofers, contractors, auto repair shops, and more. You forward your business line to your Yapper number. The AI answers every call 24/7, sounds human, books appointments, captures lead info, and transfers real emergencies straight to your cell. After every call, you get an email summary with the caller's name, number, and what they needed.

Visit product View on TrustMRRApr 6, 2026SaaS Software stripe

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Actual performance

Did not reach leaderboard

Traction signals

Domain Rating14

Launch Intel predictions

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

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

34%34% predicted probability of success on AppSumo, 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

32%32% 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

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

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

26%26% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

23%23% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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