Bustem, Inc

TrustMRR

Bustem is an AI-powered brand protection platform built for e-commerce brands. We find and take down counterfeit products, copycat websites, unauthorized sellers, stolen content, fake ads, and other IP infringement across the internet. We combine proprietary software with a hands-on enforcement team to protect 150+ brands across 38+ platforms.

Visit product View on TrustMRRJan 2, 2025E-commerce Software B2B

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Actual performance

148customers

$415,238MRR/mo

Made the leaderboard

Traction signals

Domain Rating31

MRR growth 30d+6.0%

Launch Intel predictions

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

71%71% 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 HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

52%52% 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: io · Missing: https docs, excited, just released

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

29%29% 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

19%19% 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

18%18% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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