Revenue Agents that monitor churn, upsell, and deal risk

Hacker News

I've spent most of my career leading GTM teams, and there's one blind spot that I've watched repeat at every company usage sit in one tool, deals sit in a second, support sit in a third and none of them tell the whole truth about customers. So we built Rimplo to close that gap set of revenue agents that runs across CRM, billing, support and product data and watch for three important moments customers about to churn, deals ready to expand and deals going quite in the pipeline. Also we have very powerful ai assistant that replies on anything from the GTM data sources connected powered with the top models so users decide which AI model gets assigned to each task or question. We would love to hear your feedback on the product or even if you think anything is missing.

Visit product View on Hacker NewsAug 27, 2026

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Actual performance

3points

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor

89%89% 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.

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

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

15%15% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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