SEOzapp

TrustMRR

SEOzapp helps businesses improve their visibility across AI search engines such as ChatGPT, Gemini, Google AI overviews, and Perplexity, along with traditional SEO. It audits websites for AI-search readiness, identifies technical and content gaps, provides evidence-based recommendations.

Visit product View on TrustMRRFeb 1, 2026Marketing Other B2B

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Actual performance

Did not reach leaderboard

Traction signals

Domain Rating5

MRR growth 30d-100.0%

Launch Intel predictions

Analyze your own launch →

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

80%80% 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: gemini · Missing: supports, reddit linkedin, podcasting

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

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

nativeThis product was originally launched on this platform.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

27%27% 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, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Correct prediction on native model

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