MyHair AI

TrustMRR

MyHair.AI is an AI-powered platform for hair loss analysis and tracking. Users can scan their hair, monitor changes over time, and receive personalized insights and recommendations. The product includes proprietary technology, APIs, a polished consumer experience, and a strong premium domain in a large and growing hair health market.

Visit product View on TrustMRRJun 2, 2025Health & Fitness Software B2C

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Actual performance

189customers

$782MRR/mo

Did not reach leaderboard

Traction signals

Domain Rating45

MRR growth 30d-11.9%

Launch Intel predictions

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

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

60%60% 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

38%38% 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: io · Missing: https docs, excited, just released

29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Incorrect prediction on native model

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