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Correctify – A tool for proofeading restaurant menus and documents

Hacker News

Correctify – A tool for proofeading restaurant menus and documents

After noticing the struggle that many hospitality professionals go through when proofreading menus we decided to build Correctify. Hotels and restaurants have many employees manually checking every word, a process that takes hours. Correctify is easy as a cake to use and is optimized to understand and correct culinary terminology and brands. Also, we couldn't find adequate alternatives for proofreading pdf documents in general therefore we would like to expand to many more use cases such as reports, presentations, essays, ebooks etc.

Visit product View on Hacker NewsAug 15, 2023

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

55%55% 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.

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

47%47% 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

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

nativeThis product was originally launched on this platform.

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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

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

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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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