Contentrain
Use case

Give AI agents bounded content operations

Agents can create, update, translate, search, and validate content through deterministic tools instead of editing source strings directly.

Give AI agents bounded content operations

Governance

AI content needs an operating boundary

If agents can freely edit copy, schemas, translations, and docs, quality depends on prompts alone. Contentrain gives agents tools, rules, validation, and review paths that define acceptable operations.

  • MCP tool boundaries
  • Shared rules and skills
  • Validation before delivery
AI content needs an operating boundary

Context

Make project context visible to the agent

The .contentrain project files describe models, context, vocabulary, content, and metadata in the repo. Agents can inspect the content system before they generate or modify product copy.

  • Project context file
  • Vocabulary and model definitions
  • Content files readable in Git
Make project context visible to the agent

Quality

Use rules for quality, not taste

The rules package covers content quality, SEO, i18n, accessibility, media, schema, security, normalize, and workflow expectations. That turns subjective review into repeatable checks.

  • SEO title and description guidance
  • Locale and schema validation expectations
  • Security and MCP usage rules
Use rules for quality, not taste

Approval

Require human review for production changes

Content governance is not complete until humans can approve important changes. Studio branch review, diff views, roles, and branch health checks make that possible for AI-assisted content.

  • Branch detail views
  • Reviewer and editor paths
  • Health checks before merge
Require human review for production changes

Lifecycle

Govern the whole content lifecycle

The same operating model can cover extraction, writing, translation, media, delivery, forms, webhooks, and external conversation APIs. That is why Contentrain is more than a prompt guideline.

  • Normalize and translation workflows
  • Studio media and delivery surfaces
  • Conversation API for controlled external operations
Govern the whole content lifecycle

Common questions

What is AI content governance?

It is the system of tools, rules, validation, permissions, and review workflows that controls how AI creates or changes product content.

Why is Git important for governance?

Git gives content changes history, diffs, branches, and review habits that engineering teams already trust.

Can governance start locally?

Yes. The MIT packages let a developer start with local MCP, rules, validate, normalize, and generated access before Studio is needed.

Start local. Scale to Studio.

Build a governed content layer before content becomes product debt.

Developers can start with the MIT packages. Teams can move into Studio when review, roles, delivery, and licensing matter.

Open Studio