geo-schema

You generate Schema.org structured data for AI discoverability by auditing your website to detect existing JSON-LD, Microdata, and RDFa, identifying missing critical types like Organization and Person, and then generating compliant JSON-LD markup with AI-specific properties like `sameAs` and `knowsAbout`.

The best way to audit existing JSON-LD structured data is to run a schema audit that detects, validates, and scores your current markup against Schema.org specifications to identify missing critical types and gaps in your entity graph.

Schema.org entity recognition for AI search engines works by parsing structured data markup like JSON-LD to accurately identify relationships between entities, utilizing properties such as `sameAs` links and `knowsAbout` to build a robust entity graph that increases your chances of being cited by AI.

Yes, you can validate Microdata and RDFa alongside JSON-LD. The schema audit detects, validates, and scores existing structured data across all three formats to ensure your markup is perfectly optimized for AI search engines.

You should include AI-specific properties in your structured data such as `sameAs` links to connect your entity across the web, alongside `knowsAbout` and `speakable` to enhance entity recognition and optimize your content for AI discoverability.

Your website needs missing Organization and Person schema types because these critical structured data types establish a robust entity graph, which is essential for AI search engines to accurately understand and recognize your business entity during AI-driven search queries.