AI that improves your app from all product data
Connect product signals once. The Growth Engineer ranks the next improvement and outputs reviewed issues, PR plans, or notifications.
Growth loop preview
Agent workflow in 60 seconds
OpenClaw and the Growth Engineer: signals in, reviewed issues and PR plans out.
Use all product data to let the Growth Engineer automatically find app improvements and create reviewed issues, PR plans, or notifications.
Who this is for
Start here if the problem below sounds familiar. You do not need every connector on day one—just enough signal for the Growth Engineer to propose work you can review.
- Founders who want AI to read all product signals first
- Subscription apps with onboarding, paywalls, and churn
- Teams that want agent work grounded in production data
How it works
The loop is the same across guides: connect evidence, let the agent read it, then ship a reviewed task with a verification metric.
- Track activation, onboarding, paywall, purchase, and retention events.
- Connect RevenueCat, Sentry, App Store Connect, feedback, and GitHub.
- Run the Growth Engineer on your schedule.
- Review the task, evidence, and verification KPI before shipping.
What you get back
The output should be concrete enough to review without opening five dashboards.
- Built for full product context—not dashboard screenshots.
- Outputs: GitHub issues, PR handoffs, or notifications.
- Humans review before anything ships.
Common questions
Quick answers before you connect product data to an agent.
Can AI automatically improve my app with AnalyticsCLI?
It analyzes connected data and drafts improvement tasks. Humans should still review before shipping.
What product data can the Growth Engineer use?
AnalyticsCLI events plus revenue, crashes, feedback, reviews, store data, and GitHub context when connected.
What does an automatic app improvement look like?
Usually an issue or PR plan with the problem, evidence, affected surfaces, proposed change, and verification metric.