iOS app for learning Arabic with AI-powered practice features. $1,1M+ proceeds and 407K users

Acquire.com

AI-powered Arabic and Quran learning app for iOS and Android. Users access structured lessons, AI conversation practice, and translation tools to improve their understanding of Quranic Arabic.

AI-powered Arabic and Quran learning app for iOS and Android. Users access structured lessons, AI conversation practice, and translation tools to improve their understanding of Quranic Arabic. Features voice recognition for pronunciation feedback and personalized learning paths. Launched in June 2025, the app scaled to $1.29M in ARR, $107K in MRR, and 24,670 active subscribers in 12 months. MRR grew to $107K. TTM revenue of $1.78M (all platforms) with $500K net profit. Tech stack includes Rea...

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Actual performance

Did not reach leaderboard

Traction signals

Asking price$2,490,597

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

86%86% 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 · Strong signals: ios, personal, month · Missing: mobile apps, entrepreneurs, apps

79%79% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

62%62% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

46%46% 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

40%40% 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

31%31% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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