PdfGPT

TrustMRR

PdfGPT is an AI-powered PDF chat and summarization platform with 166,218 registered users (99% verified emails), 51 paying subscribers, and ~$542 MRR (~$6.5K ARR). The product generates organic traffic through Google, ChatGPT, Copilot, and Perplexity, with AI referrals delivering the highest engagement. It has proven demand across multiple languages and regions, plus a strong student user base (2,548 identified students). Operated solo with minimal overhead, the biggest upside lies in SEO.

Visit product View on TrustMRRJul 29, 2022Artificial Intelligence IT Services B2B

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Actual performance

39customers

$412MRR/mo

Did not reach leaderboard

Traction signals

Domain Rating25

MRR growth 30d-4.0%

Launch Intel predictions

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

75%75% 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 · Missing: supports, reddit linkedin, podcasting

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comFits revenue-generating acquisition profile · Strong signals: arr, mrr, subscribers · Missing: revenue, profit, saas

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

Product HuntUnlikely to reach the leaderboard · Strong signals: google, user, email · Missing: mac, agents, macos

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

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

33%33% predicted probability of success on AppSumo, 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

27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

26%26% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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