AI-READY CMS

The CMS your AI tools can actually read

The CMS built for agentic workflows. CloudCannon keeps your entire project — code, content, and config — in a single Git repo as plain files. AI agents can read, write, and deploy without translation layers or proprietary schemas standing in the way.

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Diagram of development flow using AI and CloudCannon

How do you let AI agents make changes safely?

Git gives AI agents something no proprietary system can: a safe, auditable way to contribute using the same workflow as human developers. Agents can propose changes; devs can review, approve, or adjust them. Your file history is always preserved.

Screenshot of Git workflow in CloudCannon

Editors get a visual interface. Developers keep the file system.

CloudCannon gives editors a visual, page-based editing experience, without changing how the project is structured underneath.

Developers keep working in code. Everything syncs through Git — and this flat-file architecture also gives AI agents full project context.

What does an AI agent see in a CloudCannon project?

LLMs need context, not just content. Point an AI agent at a CloudCannon project and it sees everything. Templates, content files, configuration, styles, build pipelines — all sitting in a Git repository, all readable as plain text. Agents can trace how a content change affects layout, spot where a config tweak might break a build, and suggest fixes with full context.

Illustration of AI-using developer with full context over all files

Use the format AI already knows

LLMs are trained overwhelmingly on Git repositories. Markdown, frontmatter, YAML, folder structures, diffs — this is their native language. A CloudCannon project is already in that format. There's no translation step, no database, and no API calls to choreograph — unlike WordPress or API-based headless CMSs.

Can you use your own AI tools and models?

CloudCannon projects are local-first. Clone the repo and work on your own machine with whatever AI tooling you prefer — Cursor, Claude Code, GitHub Copilot, a locally hosted model, or something you built yourself.

For teams with data sensitivity requirements, your content stays where you put it. For everyone else, it means more flexibility and fewer dependencies.

Developer using a range of AI tooling with their IDE

How do you migrate an Astro site with an AI agent?

CloudCannon's agent-skills repo is an open-source set of AI coding agent skills that automate the process of migrating Astro projects to CloudCannon.

You install the skills into your project, open it in an agent-compatible IDE, and ask the agent to handle the migration — it'll work through auditing your site's content structure, generating CloudCannon config files, restructuring content for CMS compatibility, adding visual editing support, and running build verification.

The skills are composable: you can run a full end-to-end migration or use individual skills (like cloudcannon-configuration or cloudcannon-visual-editing) for specific tasks on an existing CloudCannon site.

View the agent skills repo →

Screenshot of analytics for a CloudCannon site

Fast by default

CloudCannon builds static sites. Pages load instantly because there's no server-side rendering, no database queries, no runtime overhead. Just pre-built HTML served from a CDN.

Your AI tools benefit from this too. A static site built from flat files is a simpler, more predictable structure — fewer moving parts means fewer things for an agent to misunderstand.

It's important to me that CloudCannon is a Git-based CMS. I hate having a vendor lock with API-based CMSs.

Alexander Luttringer

Technical Director

Croissant & Baguette

Zero maintenance, forever

Static sites don't need server runtimes, security patches, or database upgrades. There's nothing to go down at 2am. For agencies managing dozens of client sites, this is the difference between a portfolio that scales and one that drowns in upkeep.

Hosting costs stay low, too. You're serving files, not running infrastructure.

Screenshot of CloudCannon's Visual Editor and active users

Three steps to AI-ready content management

Build your site with any AI tool

Build locally with your preferred static site generator. Use Cursor, Claude Code, Copilot, or any agent that can read a file system — they'll have full context from day one.

Configure with CloudCannon

Push to Git. CloudCannon syncs your repo, builds your site, and gives editors a visual interface — while your project stays in the format AI tools already understand.

Hand off with confidence

Give editing access to non-technical editors who can update content visually — without touching your code.

Frequently asked questions

Yes. Agents work in your Git repository like any other contributor. They propose changes on a branch, and those changes go through the same review as any other pull request. Your file history keeps a record of what was changed and by whom.

No. CloudCannon reads your existing repository and builds the site from it. Templates, content files, and configuration stay where they are, in the formats you already use.

Any tool or agent that can read a file system. Clone the repo and use Cursor, Claude Code, GitHub Copilot, a locally hosted model, or something you built yourself. Projects are local-first, so you're not limited to whichever assistant a CMS vendor decides to ship.

They're an open-source set of skills for AI coding agents that automate migrating an Astro project to CloudCannon. You install them into your project, open it in an agent-compatible IDE, and ask the agent to run the migration. It audits your content structure, generates CloudCannon config files, adds visual editing support, and verifies the build. The skills are composable, so you can run the full migration or use an individual skill like cloudcannon-configuration on a site that's already set up.

In your Git repository, as plain files: markdown, frontmatter, YAML, and whatever else your site generator uses. You can clone the repo, work on it locally, and take it elsewhere whenever you want.

Yes. Editors get a visual, page-based interface in CloudCannon's Visual Editor and never need to open the repository. Their changes commit back to Git, so developers and agents keep working with the same files.

LLMs are trained overwhelmingly on Git repositories, so markdown, YAML, folder structures, and diffs are their native language. With an API-based CMS, an agent works through endpoints and can't trace how a content change affects a template or a build. In a CloudCannon project, all of it sits in the same repository as plain text.

It keeps serving. There's no server runtime, database, or dependency stack to patch, so a site can sit untouched and still load as fast as it ever did.

Give your AI the full picture.

Your code and content stay in Git.
Your repos stay yours.

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Tiaan Fairchild

Software Engineer

A friendly CloudCannon dev sitting ready for a demo call