AI Study SaaS: Gamified Quizzes & Flashcards

Acquire.com

This AI study platform turns lectures, PDFs, videos, and textbooks into personalized quizzes and flashcards in seconds. Gamified, spaced-repetition learning drives 30K MAU, 22+ min sessions, and 2.5M+ questions monthly. The business runs on subscriptions with 5,500+ paying users, $461K ARR, and $190K annual profit (26% net). Lean, low-touch operations make it simple to run and ready to scale. Key Highlights ✅ $731K TTM revenue; $461K ARR; $190K profit ✅ Engagement: 30K MAU, 22+ min sessions...

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Actual performance

$38,416/yr

Made the leaderboard

Traction signals

Asking price$800,000

Annual revenue$461,000

Annual profit$190,000

Revenue multiple1.7x

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

79%79% 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: personal, video, month · Missing: mobile apps, ios, entrepreneurs

74%74% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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.

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

34%34% predicted probability of success on Hacker News, 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.

Correct prediction on native model

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