RejectFix

TrustMRR

RejectFix is a web app for iOS and Android developers who’ve been rejected by App Store or Google Play review. You paste the rejection message, add context (app name, guideline category, subscription/login details), and RejectFix generates: A professional reply to send back to the review team Resubmission notes summarizing what changed A step-by-step fix checklist to verify before resubmitting It also tracks cases in a dashboard, scores rejection risk, lets you save and reuse replies, and incl

Visit product View on TrustMRRSep 18, 2024Developer Tools IT Services B2C

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Actual performance

8customers

$44MRR/mo

Did not reach leaderboard

Traction signals

Domain Rating6

MRR growth 30d+50.0%

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: google, context, notes · Missing: mac, agents, macos

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, google · Missing: mobile apps, personal, entrepreneurs

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

36%36% predicted probability of success on Acquire.com, 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

31%31% 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

23%23% 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

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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