Structured Data
Structured data is standardised code added to web pages that explicitly labels business information in a format AI tools and search engines can read and process programmatically.
Explained
Websites are built for humans to read, but AI tools need machine-readable signals to understand what a business does, where it operates, and who it serves. Structured data bridges this gap by encoding key business information in standardised formats that AI tools can process without interpretation. While closely related to schema markup, structured data is the broader concept encompassing all machine-readable data formats, from JSON-LD and Microdata to Open Graph tags and meta attributes, that help AI systems extract facts about your business.
Structured data removes ambiguity for AI tools
A paragraph describing your services requires an AI tool to interpret natural language, infer meaning, and decide what is factual. A structured data element that explicitly states your service type, location, and operating hours removes that interpretation step entirely. The AI knows exactly what you offer because you have told it in a format designed for machines. Less ambiguity means more confident recommendations.
Key structured data formats for GEO
JSON-LD schema markup is the most impactful format for AI visibility. It covers business type, services, location, FAQs, and reviews. Open Graph and Twitter Card meta tags control how your content appears when shared or previewed. HTML meta descriptions and title tags, while simpler, are also structured data that AI tools use during retrieval. Each format serves a different purpose, and a comprehensive GEO strategy implements all of them.
Structured data powers rich results and AI citations
Beyond AI recommendations, structured data enables rich results in Google search: star ratings, FAQ dropdowns, business information panels, and event listings. These rich results increase visibility in both traditional and AI-enhanced search. When Google's AI Overviews pull information for a response, they preferentially draw from pages with structured data because the facts are already labelled and verified.
Implementation without maintenance creates risk
Structured data that was accurate when implemented but has since become outdated is worse than no structured data at all. If your schema says you are open until 6pm but you changed your hours a year ago, AI tools will cite incorrect information. Structured data requires periodic review to ensure it reflects your current business details. AireStream includes structured data audits in its ongoing GEO management.
What this means for your business
AireStream implements and maintains structured data as a core technical component of every GEO programme. The initial audit identifies missing or incorrect structured data, and the onboarding process includes full implementation across all key pages. Ongoing quarterly reviews ensure your structured data stays current and comprehensive.
FAQ
Common Questions
Glossary
Related terms
Schema markup is structured data code added to a website that tells AI tools and search engines exactly what a business is, what it does, and who it serves.
Entity clarity is the degree to which AI tools can accurately and confidently describe what a business does, who it serves, and what makes it different from competitors.
AI trust signals are the verifiable indicators of credibility, consistency, and authority that AI tools evaluate before recommending a business in their responses.
AI readability is the ease with which AI tools can parse, understand, and extract accurate information from a piece of content or a website.
Retrieval-augmented generation (RAG) is a technique where AI tools fetch real-time information from external sources before generating a response, rather than relying solely on their training data.