Arc
TrustMRRARC is a premium circadian rhythm and sleep optimization app live on the iOS App Store. It helps users discover their chronotype (Lion, Bear, Wolf, Dolphin) and align their daily focus and caffeine in
ARC is a premium circadian rhythm and sleep optimization app live on the iOS App Store. It helps users discover their chronotype (Lion, Bear, Wolf, Dolphin) and align their daily focus and caffeine intake with their natural energy peaks.
Share cardActual performance
12customers
$48MRR/mo
Did not reach leaderboard
Traction signals
MRR growth 30d+17.6%
Launch Intel predictions
Analyze your own launch →TrustMRRFits verified-revenue profile · Strong signals: ios, users · Missing: mobile apps, personal, entrepreneurs78%78% 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 · Strong signals: ios · Missing: supports, reddit linkedin, podcasting74%74% 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, macos37%37% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released28%28% predicted probability of success on Hacker News, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue27%27% 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, crypto19%19% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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