Launch Intel
AgenSea

AgenSea

TrustMRR

AgenSea is an all-in-one management SaaS built for creator agencies. It helps agencies manage creators, organize content, assign tasks, track social performance, run SFS campaigns, calculate revenue a

AgenSea is an all-in-one management SaaS built for creator agencies. It helps agencies manage creators, organize content, assign tasks, track social performance, run SFS campaigns, calculate revenue and expenses, store SOPs, onboard team members, and scan model history or reposted content through AI. The platform is live, usable, and already has a few trial users testing it. It also includes pricing, authentication, dashboards, subscription structure, and multiple agency-focused tools.

Visit productView on TrustMRRAug 29, 2025SaaSSoftwareB2B
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Actual performance

3customers
$527MRR/mo
Did not reach leaderboard

Traction signals

Domain Rating3
MRR growth 30d-100.0%

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, tasks · Missing: mac, agents, macos
63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal
62%62% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: revenue, saas, subscription · Missing: arr, mrr, profit
48%48% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
32%32% predicted probability of success on AppSumo, 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
21%21% predicted probability of success on Hacker News, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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