Corrigoo

TrustMRR

Corrigo est un logiciel de correction et de reformulation de texte assistée par intelligence artificielle. Les clients téléchargent une application de bureau (Mac) et souscrivent un abonnement mensuel de 9 € ou 13€. L'application corrige, reformule, simplifie, dicte et traduit le texte sélectionné via un raccourci clavier, dans n'importe quelle application. Vente 100 % en ligne, livraison numérique immédiate (clé de licence par e-mail).

Visit product View on TrustMRRJul 2, 2026Productivity Software B2C

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Actual performance

2customers

$20MRR/mo

Did not reach leaderboard

Traction signals

Domain Rating2

MRR growth 30d-0.1%

Launch Intel predictions

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67%67% predicted probability of success on BetaList, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

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

59%59% 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

36%36% 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

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

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

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

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

23%23% 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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