The Claw that Built Itself: How we used amazeeClaw to ship amazeeClaw

A few weeks ago, OpenAI shared that ChatGPT helped to build ChatGPT. It was a pretty cool headline. But at amazee.ai, we weren't just inspired by it, we were already living it, using our own amazeeClaw product to help build itself. And not in an abstract way. We literally shipped production features, managed infrastructure, and ran marketing, all through the product we were building.

Here's the story:

What is amazeeClaw?

amazeeClaw is amazee.ai's managed OpenClaw hosting service. OpenClaw is fast becoming the go-to platform for teams building and running autonomous AI agents. The ecosystem is exploding, but the hosting landscape is fragmented, with dozens of small, uncertified providers, no enterprise-grade option, and no one offering data sovereignty or bundled LLM access.

We developed amazeeClaw to change that. We built it on top of amazee.io infrastructure with a clear focus:

• Ease of use: Deploy in under 60 seconds
• Private container isolation: No shared tenancy, no data co-mingling
• Data sovereignty: EU, CH, UK, US, and AU data center choices
• Certified infrastructure: ISO 27001 + SOC 2 Type II
• Bundled LLM access: Private, regional AI model endpoints via amazee.ai Gateway

But the best way to prove a product works is to use it yourself. So we did, extensively.

Use Case 1: Building the Product

Before the full launch, we ran an alpha to test our ideas and get feedback. The alpha dashboard, the actual interface where users spin up their amazeeClaw instances, was built almost entirely by an amazeeClaw agent.

Here's how the workflow looked:

First, I set up an amazeeClaw agent with access to three things:

1. A Git repository for the codebase
2. Notion for task management
3. OpenCode (a coding IDE with OpenClaw integrations, powered by amazee.ai LLM keys)

From there, I created a simple Kanban board in Notion. Whenever I wanted a new feature or a tester had a request, I added a task to the "To Do" column. That was basically my only job in the development process.

The amazeeClaw agent monitored that board. When a new task appeared, it spawned a sub-agent dedicated to that task. The sub-agent fired up OpenCode, wrote the code, wrote automated tests, created a pull request in the Git repo, moved the Notion task to "Review," and left a summary of what it did. All I had to do was review the PR, run the tests, and hit merge.

The turnaround was remarkable. When a user asked for a multi-line input field via Slack, I copied the request into Notion, and within 10 minutes, the feature was deployed. Not planned. Not scheduled for the next sprint. Deployed.

This was possible because OpenClaw connects everything: task management, code execution, version control, and sub-agent orchestration. Could I have built all of this myself? Sure. But it would have taken ten or twenty times longer.

Use Case 2: Managing Infrastructure at Scale

Building features was only half the challenge. During the alpha, we had multiple amazeeClaw instances running for different testers, all hosted on amazee.io via our open source application delivery platform.

Managing all of those manually would have been a nightmare. We had to handle:

• Deploying updates across the board
• Verifying versions and debugging failures
• Checking LLM key budgets
• Monitoring user activity

Usually, these tasks require logging into each individual instance to run checks and cross-reference data. To solve this, I created a second agent called the "Master Claw." This agent had access to an amazee.io user account that could see every amazeeClaw instance in the environment.

Whenever I released a new version, I simply told the Master Claw to deploy it everywhere. It rolled out updates in batches of five, verified the successes, and reported back. If a deployment failed, it analyzed the logs to find the root cause and flagged it for me. It could even SSH into individual instances to check system health or see if a user had hit their token budget. I managed the entire fleet in a single conversation.

This is a big shift for our customers. If you manage dozens or hundreds of sites on amazee.io, you can give an amazeeClaw agent access to amazee.io and let it monitor, deploy, and troubleshoot across your entire fleet.

Use Case 3: Marketing on Autopilot

The third way amazeeClaw built amazeeClaw was through marketing.

I set up another agent connected to my LinkedIn account. I gave it context about amazeeClaw, such as positioning, USPs, and launch timeline, and asked it to plan pre-launch activities. We iterated on the ideas together in conversation. Once we aligned on the content, the agent scheduled and published the posts directly to LinkedIn.

No logging into LinkedIn. No copy-pasting from a doc. Just a conversation with an agent who understood the product and had the tools to act on it.

This pattern extends naturally. Connect an agent to other social platforms, your blog CMS, or your email marketing tool. The orchestration layer does the heavy lifting.

(And yes, the irony is not lost on me. Who knows, maybe this blog post was drafted by amazeeClaw too.)

The Multiplier Effect: Buying Back Time with AI Agents

Whether it was development, infrastructure, or marketing, the real value was leverage. I run a company. My days are packed with coordination, strategy, customer conversations, and a hundred other things. I don't have the time to spend hours manually deploying updates, writing LinkedIn posts, or hand-coding dashboard features.

Using amazeeClaw didn't just help us build the product; it also helped us build the team. It gave me the capacity to actually lead the company while the platform handled the heavy lifting. It gave me background agents that worked while I focused on other things. When something needed my attention, they pinged me in Slack. When something went wrong, they told me why. When a user had feedback, the fix was live in minutes.

All of these agents lived in Slack channels, which means I could also grant access to teammates. The LinkedIn agent, the development agent, the Master Claw; any of them could be shared with the right people on the right channels. It's collaborative AI operations, built on trust and access control.

The result: I was 10-20 times faster throughout the entire alpha launch than doing everything manually.

The Roadmap: What's Next for amazeeClaw

We're just getting started. Here is a look at what we're working on right now:

• Support Claw: a dedicated agent that answers questions about amazeeClaw, troubleshoots issues, and can even restart broken instances automatically
• Setup Orchestrator: an agent that helps small teams spin up and configure multiple amazeeClaw instances, pre-filled with company context, team info, and roles
• Broader integrations: more tools, more platforms, more ways for agents to act on your behalf

The goal is simple. You get an orchestrator that connects to your tools, understands your context, and executes autonomously, while you stay in total control.

Your Agents. Your Infrastructure. Your Rules.

amazeeClaw is more than just another hosting service. It's a sovereign, certified, enterprise-ready platform for running AI agents. We know it works because we literally used it to build and launch the product itself.

If you want to run AI agents without compromising on data sovereignty or security, amazeeClaw was built for you.

amazeeClaw is built on amazee.io infrastructure — ISO 27001 and SOC 2 Type II certified, with data centers in EU, CH, UK, US, and AU. LLM access is provided through amazee.ai gateway with private, regional endpoints.