Directorly

TrustMRR

Directorly is a SaaS platform that enables businesses and organizations to create AI-powered business directories for their local communities or industry niches. Customers discover us through digital

Directorly is a SaaS platform that enables businesses and organizations to create AI-powered business directories for their local communities or industry niches. Customers discover us through digital marketing channels and word-of-mouth referrals, then use our intuitive interface to build comprehensive directories with automated data collection and intelligent search capabilities. We operate on a tiered subscription model with plans, charging based on the number of directories, l

Visit product View on TrustMRRJul 26, 2025SaaS Software B2B

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Actual performance

30customers

$1,503MRR/mo

Made the leaderboard

Traction signals

Domain Rating27

MRR growth 30d+73.7%

Launch Intel predictions

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

79%79% 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.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

63%63% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: platform, intuitive, interface · Missing: plus, reviews, host

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% 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

25%25% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

18%18% 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

11%11% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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