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To optimize content for AI search discoverability, you need to audit AI bot access, validate JSON-LD structured data, and ensure proper content structure. This involves analyzing robots.txt rules and generating schema markup to make content easily crawlable and citation-ready for AI engines.

A GEO audit is a structured assessment of your site's AI search readiness, evaluating AI bot access, structured data quality, and content discoverability. It generates actionable fixes for robots.txt and schema gaps to improve how AI systems crawl and cite your content.

You can check JSON-LD readiness by running a project scan that assesses schema quality, identifies structured data gaps, and verifies content structure. The audit generates specific fixes for missing or incorrect schema markup aligned with GEO workflow requirements.

An llms.txt file improves AI search discoverability by providing explicit guidance to AI crawlers. The GEO audit checks for llms.txt presence and generates actionable fixes to enhance robot access and citation readiness across major AI systems.

Yes, you can audit websites, documentation, and code repositories for AI search optimization. The scanner assesses AI bot access and structured data across project directories to guide discoverability improvements for various content types.

The best way to fix robots.txt issues is to run a GEO audit that analyzes AI bot access rules and identifies blocking configurations. The audit provides actionable fixes for robots.txt to ensure major AI systems can properly crawl your content.