Schema Markup
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.
Explained
Schema markup is a standardised vocabulary of code, typically written in JSON-LD format, that sits in a webpage's HTML and labels its content for machines. Where a human reader can infer that a page belongs to a law firm from context and tone, an AI tool benefits from explicit declarations: LegalService, LocalBusiness, FAQPage, and Organisation schemas remove any ambiguity. For UK SMEs trying to appear in AI-generated recommendations, schema markup is one of the highest-leverage technical changes available.
What schema markup actually does
Schema markup wraps your business information in a structured format that AI tools and search engines can parse programmatically. Instead of guessing what your business does from body copy, an AI can read a machine-readable label that says: this is a LegalService, operating in Manchester, serving individuals with employment disputes. That precision directly improves how accurately AI tools describe and recommend you.
The most useful schema types for GEO
For UK SMEs, the highest-impact schema types are: Organization (your name, address, phone, logo, and founding date), LocalBusiness or its subtypes (AccountingService, LegalService, MedicalBusiness), FAQPage (question-and-answer pairs AI tools love to surface), and Service (describing individual offerings with descriptions and pricing). Each one adds a layer of entity clarity that makes you easier for AI tools to cite confidently.
JSON-LD is the preferred format
Schema markup can be implemented in several formats, but JSON-LD is the format recommended by Google and most widely parsed by AI systems. It sits in a script tag in the page head, separate from the visible HTML, which makes it easy to add or update without redesigning your pages. Most modern website platforms support JSON-LD either natively or via plugins.
Schema markup without content consistency fails
Schema markup amplifies what is already on your site. If your schema says you are based in Leeds but your footer says Yorkshire with no town listed, the conflict creates entity confusion rather than clarity. Schema works best as part of a broader GEO strategy where your NAP (name, address, phone), service descriptions, and structured data all say the same thing across every page and platform.
What this means for your business
AireStream audits and implements schema markup as part of every AireStream audit. Correct schema markup is one of the first technical fixes applied during an engagement because it provides an immediate, measurable improvement in how clearly AI tools can describe a client's business. It also underpins the entity clarity work that drives long-term AI search visibility.
Further reading
FAQ
Common Questions
Glossary
Related terms
Generative Engine Optimisation (GEO) is the practice of optimising a business's online presence so that AI-powered tools - such as ChatGPT and Google's AI Overviews - recommend it in response to relevant queries.
AI search visibility is a measure of how frequently and prominently a business appears in AI-generated answers when relevant questions are asked.
LLM citations are the source references that AI language models include in their responses - links or mentions of specific content that informed the generated answer.
AI search ranking refers to how AI tools order and prioritise the businesses, content, or information they include in generated responses - determining not just whether you appear, but how prominently.
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.