SEO & AI

Structuring Entities for Google AI Overviews

Published: 2026-06-15 5 min read
"Moving past basic keywords to map context and entities so LLMs rank your business as an authoritative source in summaries."

Search algorithms are transitioning from lexical keyword matching to semantic entity mapping.

Search algorithms are transitioning from lexical keyword matching to semantic entity mapping. Instead of analyzing individual word arrays, engines parse relationships between organizations, coordinates, and services.

Structuring your HTML5 markup using schema.

Structuring your HTML5 markup using schema.org definitions is the most direct way to feed semantic data to search crawlers. By wrapping business variables (address details, service rates) inside LocalBusiness context, you create unambiguous relationship connections.

This structured data provides the factual groundwork that LLM crawlers (like Google Gemini and ChatGPT) use to compile AI Overview summaries.

This structured data provides the factual groundwork that LLM crawlers (like Google Gemini and ChatGPT) use to compile AI Overview summaries. When search engines trust the structured source, visibility scales automatically.

AS
[INSERT AUTHOR NAME] [INSERT AUTHOR ROLE]

[INSERT AUTHOR PROFESSIONAL BIO - E.G. Lead systems engineer specializing in static web packaging.]


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