Hey IH 👋
Wanted to share a quick update on what we've been building with Rapid Claw.
The short version: We built a managed platform that lets you deploy OpenClaw AI agents in under 60 seconds — no DevOps, no server config, no Docker headaches.
How we got here
About 6 months ago I was trying to set up my own OpenClaw instance. Took me an entire weekend just to get the networking right. I kept thinking — if this is hard for me, how is a non-technical founder supposed to use AI agents at all?
So I started building a one-click deploy. The first version was embarrassingly simple — basically a shell script that spun up a DigitalOcean droplet and ran the install. But people loved it. The feedback wasn't "add more features" — it was "just make it work and keep it running." So that's what we focused on.
What actually worked for growth:
What surprised us:
The biggest use case isn't what we expected. We thought developers would be our main users. Nope — it's small agency owners who want AI agents handling their client emails, scheduling, and GitHub issues. They don't want to learn infrastructure. They just want the agent running.
Current numbers:
What's next:
We're building out multi-agent workflows and deeper CRM integrations (HubSpot and Shopify are already live). The goal is to make Rapid Claw (https://rapidclaw.dev) the easiest way to go from "I want an AI agent" to "my AI agent is handling my busywork" in under 3 minutes.
Happy to answer any questions about the tech stack, growth, or the managed AI agent space in general. And if you're running OpenClaw yourself and hating the maintenance — we built this for you.
The agency owner thing tracks. I work on AI deployments and the real bottleneck is never setup, it's getting people to actually trust the agent with live client stuff. What's driving that 4% churn, people who never found a use case or people whose agents just stopped being useful after week two?