TravelMaxx

TrustMRR

We are building agents for helping people book flights, hotels, and more with their points. We are also facilitating forums, post subscriptions, and news about the travel world and different hacks. We generally charge customers during checkout, and sometimes give them a free trial! It’s a subscription based model with one-off bundle purchases as well.

Visit product View on TrustMRRJun 1, 2026Artificial Intelligence Travel B2C

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Actual performance

101customers

$1,174MRR/mo

Made the leaderboard

Traction signals

MRR growth 30d+3.3%

Launch Intel predictions

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AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

45%45% 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

45%45% 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 · Missing: supports, reddit linkedin, podcasting

44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor

38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue

24%24% 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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