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Docs

AI Readiness measures how well AI systems can understand, retrieve, cite, and act on your content. The articles here cover the concepts, schema markup, and agent-metadata conventions the rubric measures — each built as a standalone citation surface for AI agents and developers. Methodology and rubric live at /methodology.

Concepts

  • Documentation Patterns Documentation Patterns measures whether your docs site uses the structural conventions AI agents recognize as documentation — schema markup, code-block markup that survives extraction, and the agent-metadata trio (llms.txt, agents.md, .well-known/mcp.json).
  • How AI agents read your docs AI agents read your docs in a mechanical four-step loop — crawl, parse, structure, cite. Every step has a way to fail, and most documentation sites fail at more than one of them.
  • AEO vs. SEO AEO and SEO measure different content properties for different consumers — Google's ranking algorithm versus AI agents like ChatGPT, Claude, and Perplexity. The two scores are independent.
  • AI Readiness AI Readiness measures how well AI systems can understand, retrieve, cite, and act on your content. It is measurable, version-pinned, and distinct from SEO.

Operational

  • How Obaron respects bot controls Obaron honors robots.txt, AI-bot directives, and Crawl-delay headers. Here's exactly what we follow, what we don't, and how to block us if you want to.

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