Yonoma – Behavior based email automation for SaaS

Hacker News

Hi HN, I've been building Yonoma ( https://yonoma.io ), an email automation tool focused on early stage SaaS teams. I used a few well known tools in this space while working with other SaaS founders, and I kept seeing the same problem. These tools are powerful, but they are also built for larger companies. Setting up simple onboarding or trial reminder flows often felt heavier than it needed to be. Many founders told me they wanted something easier that still reacts to how users behave inside the product. So I tried to build something simpler and more focused. Yonoma sends emails based on what users actually do inside your product. Things like signing up, becoming inactive, hitting an activation step, or getting close to the end of a trial. The timing is handled automatically so teams do not need to manage it manually. Here is what it supports today: - Behaviour based triggers - Onboarding, activation, trial reminders, re-engagement flows - Ready to use workflows and templates - Integrations with Stripe, HubSpot, Segment, Slack, Zapier The goal is to give small SaaS teams an easier way to set up behaviour based email automation without the complexity of enterprise tools. You can try it here (no credit card required): https://yonoma.io Happy to answer questions or get feedback.

Visit product View on Hacker NewsNov 20, 2025

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

87%87% 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: slack, user, stripe · Missing: mac, agents, macos

87%87% 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: ide, io · Missing: https docs, excited, just released

50%50% 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, way · Missing: mobile apps, ios, personal

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

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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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