Zirka: AI Tutor for Families

TrustMRR

Zirka is a family learning app built around a Socratic AI tutor that guides kids to think through problems instead of just handing them the answer. Kids sign in with a family code and PIN - no email,

Zirka is a family learning app built around a Socratic AI tutor that guides kids to think through problems instead of just handing them the answer. Kids sign in with a family code and PIN - no email, no password, no social accounts. Parents stay in the loop with notifications and reports.

Visit product View on TrustMRRJul 5, 2026Artificial Intelligence Mobile App B2C

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Actual performance

2customers

$15MRR/mo

Did not reach leaderboard

Launch Intel predictions

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

64%64% predicted probability of success on TrustMRR, 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: email, code · Missing: mac, agents, macos

45%45% 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: ide, 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 · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

19%19% predicted probability of success on Indie Hackers, 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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