Viral AI Stylist app

Acquire.com

20+ paying customers, $1k ARR w/o marketing, completed AI fashion stylist/outfit app with 5M TikTok views and 5k downloads, offering acquirers a proven viral marketing engine and ready-to-scale consumer product in the booming AI fashion market. ✅ 5M TikTok views with organic content ✅ 4k downloads on iOS ✅ Early conversion funnel data ✅ Fully built, production-ready AI stylist/outfit recommendation app

Visit product View on Acquire.com Mobile

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Actual performance

$83/yr

Did not reach leaderboard

Traction signals

Asking price$8,900

Annual revenue$1,000

Revenue multiple8.9x

Launch Intel predictions

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

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comFits revenue-generating acquisition profile · Strong signals: arr · Missing: mrr, revenue, profit

78%78% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

65%65% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% predicted probability of success on Product Hunt, 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

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

28%28% 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

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

Incorrect prediction on native model

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