Ai Search Audit

TrustMRR

AI Search Audit helps businesses improve their visibility across AI search engines such as ChatGPT, Gemini, Claude, and Perplexity. It audits websites for AI-search readiness, identifies technical and content gaps, provides evidence-based recommendations, and is evolving into an end-to-end Audit → Fix → Deploy → Verify platform.

Visit product View on TrustMRRAug 4, 2026Artificial Intelligence Other dodopayment

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Actual performance

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

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

78%78% 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.

TrustMRRLess likely to generate early MRR · Strong signals: ai, ai search · Missing: api, 000, profitable

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Strong signals: gemini · Missing: supports, created, podcasting

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

Hacker NewsMay not resonate with HN audience · Missing: https docs, just released, open source

29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

21%21% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, just like, small businesses

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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