Structured Data for AI: How to Make Your Website Machine-Readable

AI Models Read Your Website Differently Than Humans

When a human visits your website, they see your brand design, photos, and carefully crafted copy. When an AI model visits your website, it sees HTML code. The easier your website's information is to parse from that code, the more accurately AI models can understand and recommend your business.

Making your website "machine-readable" does not mean changing how it looks. It means adding structured layers of information that AI models can process efficiently.

Layer 1: Schema Markup (JSON-LD)

Schema markup is the most established form of structured data. It uses the Schema.org vocabulary to describe your business, services, products, and content in a standardised format.

For AI visibility, the most impactful schema types are LocalBusiness (your identity), Service (what you offer), FAQ (common questions and answers), Review (customer ratings), and Product (if applicable). Our schema markup guide covers implementation in detail.

Layer 2: llms.txt

An llms.txt file is a newer standard designed specifically for AI models. It sits at the root of your website (yourdomain.com/llms.txt) and provides a structured summary of your business that AI models can read instantly.

Unlike schema markup, which is embedded in your HTML, llms.txt is a standalone file optimised for AI comprehension. It includes your business summary, key facts, products and services, and links to detailed content. Our llms.txt guide provides the full specification and implementation steps.

Layer 3: Clean HTML Structure

Beyond explicit structured data, the HTML structure of your website matters. AI crawlers parse your HTML to extract information, and clean structure makes this more reliable.

Use semantic HTML elements. H1 for page titles, H2 for major sections, H3 for subsections. This hierarchy tells AI models how your content is organised.

Use descriptive alt text on images. AI models cannot see images, but they read alt text to understand what images depict.

Keep important content in text, not images. Text embedded in images or infographics is invisible to AI models. If key information (pricing, services, contact details) is only in an image, AI models cannot access it.

Avoid hiding content behind JavaScript interactions. If critical content only loads when a user clicks a tab or expands an accordion, AI crawlers may not see it. Ensure your most important content is visible in the initial HTML.

Layer 4: MCP Servers (Advanced)

MCP (Model Context Protocol) servers are the most advanced form of structured data for AI. An MCP server provides a queryable API that AI models can call to get real-time, structured information about your business.

This is beyond what most businesses need initially, but for those seeking maximum AI visibility, MCP servers offer the highest level of control over how AI models represent your business. Our MCP server guide explains how they work.

Priority Order for Implementation

For most Singapore businesses, the implementation priority should be schema markup first (the foundation), then llms.txt (quick to implement, high impact), then HTML structure improvements (ongoing maintenance), and finally MCP servers (for businesses wanting maximum control).

Start with schema markup and llms.txt. Together, these give AI models the structured information they need to understand and recommend your business accurately. The other layers add incremental improvement.

Testing Your Implementation

After adding structured data, test whether AI models can access it. Ask ChatGPT, Claude, or Perplexity about your business. If they can provide specific, accurate details about your services, hours, and location, your structured data is working.

If they provide vague or inaccurate information, review your implementation. The most common issues are schema markup with syntax errors, llms.txt files that are blocked by robots.txt, and important content hidden behind JavaScript.

For the complete AI visibility framework, see our five-dimension audit.

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