How does AI understand your website?

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Meta

Type: knowledge_hub

Language: en-GB

Last updated: 2026-10-01

Retrieval summary

What: AI systems understand websites by combining multiple signals including visible content, HTML structure, headings, internal links, metadata, accessibility, and relationships between content, rather than relying on a single technology or piece of markup.

Who

Organisation: Feel Created

Relevant for

Problems solved

Article

Type: Knowledge Hub guide

Primary topic: How AI systems understand websites through enduring web principles: clear communication, logical structure, accessibility, and technical soundness.

Intent: AI systems understand websites by combining multiple signals including visible content, HTML structure, headings, internal links, metadata, accessibility, and relationships between content, rather than relying on a single technology or piece of markup.

Audience

Topics

Content focus

Canonical claims

Item 1

Claim: AI systems that retrieve information from the web often benefit from advances in JavaScript processing, but their ability to process JavaScript varies depending on how they discover and retrieve content.

Evidence type: page_content

Evidence strength: medium

Source section: does-ai-understand-javascript

Item 2

Claim: Most major search engines can now process JavaScript and understand content that appears after the initial page load, although this may not happen immediately.

Evidence type: page_content

Evidence strength: medium

Source section: does-ai-understand-javascript

Item 3

Claim: AI builds understanding by combining many different signals, including page structure, content, internal links, metadata, accessibility, structured data, and the wider context of a website.

Evidence type: page_content

Evidence strength: medium

Source section: a-common-misconception

Item 4

Claim: Websites that are clearly written, logically organised, accessible, and technically robust help AI systems interpret information more accurately.

Evidence type: page_content

Evidence strength: medium

Source section: what-organisations-should-focus-on

Item 5

Claim: Many accessibility best practices make information clearer and less ambiguous, which naturally helps AI and other systems interpret content.

Evidence type: page_content

Evidence strength: medium

Source section: why-accessibility-helps-ai

Item 6

Claim: Structured data does not tell AI everything about a webpage, nor does it compensate for poorly written or poorly organised content.

Evidence type: page_content

Evidence strength: medium

Source section: where-structured-data-fits

Item 7

Claim: LLMs.txt complements rather than replaces established web standards such as HTML, structured data and XML sitemaps.

Evidence type: page_content

Evidence strength: medium

Source section: what-about-llms-txt

Item 8

Claim: Screen readers and assistive technologies rely on the same semantic structure that helps AI understand webpages.

Evidence type: page_content

Evidence strength: medium

Source section: ai-starts-with-structure

Common questions

Item 1

Question: Does AI understand a website because of one particular feature like headings or structured data?

Answer: No, AI builds understanding by combining many different signals, including page structure, content, internal links, metadata, accessibility, structured data, and the wider context of a website.

Item 2

Question: What is the role of structured data in AI understanding?

Answer: Structured data provides additional context by identifying particular pieces of information in a standardised way, such as indicating that a page describes an organisation, article, event, product, or FAQ. It does not replace clear content or good website structure.

Item 3

Question: Why is accessibility mentioned in relation to AI?

Answer: Accessibility exists to make websites usable for as many people as possible, but many accessibility best practices also make information clearer and less ambiguous, which naturally helps AI and other systems interpret content.

Item 4

Question: How does information architecture help AI?

Answer: Information architecture, experienced through navigation, categories, breadcrumbs, and internal links, helps AI understand how different subjects connect across the website, establishing topics as areas of expertise rather than isolated pages.

Item 5

Question: What is LLMs.txt?

Answer: LLMs.txt is a proposed web standard designed to help AI systems identify important content on a website. It complements rather than replaces established web standards such as HTML, structured data and XML sitemaps.

Services

Zero-click and AI search optimisation

Relationship: ongoing_support

Url: /services/zero-click-ai-search-optimisation/

Search performance and technical SEO

Relationship: search_visibility

Url: /services/search-performance-technical-seo/

Information architecture

Relationship: supporting

Url: /services/information-architecture/

Content architecture

Relationship: primary

Url: /services/content-architecture/

Sections

Overview

Content

Item 1

Text: As more people use tools such as ChatGPT, Google AI Mode and Perplexity to discover information, organisations are increasingly asking the same question: How does AI actually understand what’s on my website? It’s an understandable question because we experience websites visually. We read headings, look at images, follow navigation and gradually build an understanding of what an organisation does and how its information fits together. AI doesn’t interpret a website in quite the same way. Instead, it builds an understanding from many different signals. The words on the page are important, but so are the HTML structures, headings, internal links, metadata, accessibility, relationships between content and the wider context of the website. Understanding those signals helps explain why many of the principles behind good websites remain just as important. Websites built with clear structure, useful content and good accessibility have always been easier for people to understand. Increasingly, they help AI understand them too. Unless you somehow found a way into the Grid after watching Tron, you’ve almost certainly encountered software designed to behave intelligently. Games such as Theme Park in the mid-1990s used carefully designed rules to make thousands of virtual visitors behave independently, long before today’s AI assistants became part of everyday life. The game’s co-designer and lead programmer, Demis Hassabis, would later go on to co-found DeepMind, now one of the world’s leading AI research organisations. The technology behind Theme Park was very different from today’s generative AI, but it highlights an important point. Software designed to simulate intelligent behaviour has existed for decades. What has changed is the capabilities of modern AI, its widespread adoption, and how deeply it has become woven into the technology we use every day. This includes the systems that discover, interpret, and connect information across the web. Although AI technology continues to evolve, the underlying principle remains the same. AI doesn’t rely on one signal to understand a website. Instead, it builds a picture by combining lots of different pieces of information. So how does that process actually work?

First, AI has to discover your website

Content

Item 1

Text: Before AI can understand a webpage, it first needs to know the page exists. It usually begins with automated systems known as crawlers. Search engines have used them for many years to discover webpages by following links, reading XML sitemaps and revisiting websites to identify new or updated content. Many AI systems don’t start with a fresh web crawl every time someone asks a question. Instead, they’ll often build on information they’ve already collected, with some systems also retrieving more up-to-date information where it adds value. Once a page has been found, it still needs to be processed. Modern websites are rarely just static HTML. Pages often include stylesheets, images, interactive components and JavaScript that changes what users see. Depending on how a website is built, a system may need to render the page to understand content that isn’t immediately available in the initial HTML. Finding a webpage is only the beginning. Interpreting what that page means is where the real challenge begins.

AI starts with structure

Key points

Content

Item 1

Text: Imagine walking into a well-organised library. You don’t need to read every book to understand how the library works. The shelves, categories and signs quickly tell you how information has been organised. AI approaches websites in a similar way. It first looks at how the information is organised before trying to understand what the content is actually saying. When a webpage loads in a browser, HTML provides the page's structure. Visitors notice the visual design, but HTML helps browsers and other systems understand what each part of the page is. It identifies headings, paragraphs, navigation, articles, lists, buttons and footers, helping explain how the page is organised. This is often referred to as semantic HTML. Rather than simply describing how something should look, it describes what the content actually is, giving AI and other systems clearer clues about the purpose of each element. These principles have long been considered good practice for accessibility. Screen readers and other assistive technologies rely on the same semantic structure to help people understand and navigate a webpage. That’s where semantic structure starts to make a real difference. A large piece of bold text might look like a heading to a person, but if it hasn’t been marked up as one, automated systems have fewer clues about its purpose. Likewise, a properly structured navigation menu communicates far more than a collection of visually arranged links. Headings do much more than break up a page. They create a clear hierarchy, helping readers move naturally from the main topic into the supporting information. That same hierarchy gives AI useful clues about what matters most and how the different parts of a page fit together. Structure alone doesn’t explain everything, but it provides the framework on which everything else is built.

How pages connect matters

Key points

Content

Item 1

Text: Understanding individual pages is only part of the picture. Websites rarely exist as isolated documents, and AI also looks at how pages relate to one another. Imagine visiting a town you’ve never been to before. Even without exploring every street, you quickly begin to understand how the place is organised. Signposts point you towards important landmarks, neighbouring buildings hint at what an area is for, and major roads reveal how different parts of the town connect. Websites work in much the same way. Navigation, categories, breadcrumbs and internal links all help explain how information fits together. They show which topics are closely related, which pages are considered important and how visitors are expected to move through the site. For example, a page about accessibility might sit within a wider user experience section, link to articles about inclusive design and reference guidance on WCAG. Individually, each page covers a specific subject. Together, they establish accessibility as an important area of expertise rather than an isolated topic. This broader organisation is known as information architecture. Visitors experience it through menus, navigation and page structure, while AI uses those same relationships to understand how different subjects connect across the website. Good information architecture has always improved usability. It helps people find information more quickly, reduces duplication and creates logical pathways between related content. Those same qualities also help AI build a clearer understanding of a website.

A common misconception

Content

Item 1

Text: It’s easy to assume AI understands a webpage because of one particular feature, whether that’s headings, structured data or metadata. In reality, none of those elements tells the whole story. Instead, AI builds understanding by combining many different signals. Page structure, content, internal links, metadata, accessibility, structured data and the wider context of a website all contribute. Some signals carry more weight than others, but it’s the combination that helps build a reliable understanding. That’s why there is rarely a single change that suddenly makes a website “AI-friendly”. Good understanding comes from lots of small signals working together consistently, rather than one clever optimisation.

Where structured data fits

Content

Item 1

Text: Structured data is often mentioned in discussions about AI and modern search, but it’s also one of the most misunderstood parts of a modern website. It doesn’t tell AI everything about a webpage, nor does it compensate for poorly written or poorly organised content. Instead, it provides additional context by identifying particular pieces of information in a standardised way. For example, structured data can indicate that a page describes an organisation, an article, an event, a product or a frequently asked question. It can also identify details such as publication dates, authors or locations. Imagine someone shows you a photograph of a building. You could probably work out quite a lot just by looking at it. Now imagine they add a short label underneath: “Wakefield Cathedral viewed from Westgate on a sunny afternoon” The photograph hasn’t changed. The label simply confirms what you’re looking at. It doesn’t replace clear content or good website structure. Instead, it reinforces them by providing another reliable signal about what the content represents. When combined with semantic HTML, logical information architecture and well-written content, structured data becomes another signal that helps systems interpret information more accurately. If you’d like to explore this topic in more detail, our guide, Structured Data Explained: How Search Engines and AI Understand Your Website, explains how structured data works and when to use it.

Why accessibility helps AI

Content

Item 1

Text: Accessibility and AI are increasingly mentioned together, but not because accessibility exists for AI. Accessibility exists to make websites usable for as many people as possible. It supports people who use screen readers, keyboard navigation, voice control and other assistive technologies. That remains its primary purpose. Many accessibility best practices also make information clearer and less ambiguous, which naturally helps AI and other systems interpret content. For example, a screen reader doesn’t know that a large block of bold text is meant to be a heading. It relies on the heading being marked up correctly in the HTML. AI systems benefit from that same semantic information because it provides additional clues about how the page is organised. Similarly, good alternative text helps people who can’t see an image understand the same information a sighted visitor would gain from it. That same clarity also gives AI and other systems more useful information about the content of the page. That doesn’t make accessibility an AI optimisation technique. Instead, it reflects a broader principle that runs throughout this article: content designed to communicate clearly with people often communicates more clearly with machines as well.

Does AI understand JavaScript?

Content

Item 1

Text: Modern websites are far more interactive than they were a decade ago. Content may load dynamically, navigation can update without refreshing the page and entire sections of a website may be generated using JavaScript. When implemented well, these techniques can create faster and more engaging user experiences. Fortunately, search engines have become much better at rendering JavaScript over the years. Most major search engines can now process JavaScript and understand content that appears after the initial page load, although this may not happen immediately. AI systems that retrieve information from the web often benefit from those same advances, but their ability to process JavaScript varies depending on how they discover and retrieve content. JavaScript itself isn’t usually the issue. Problems tend to arise when important information only becomes available after someone interacts with the page. If that content never loads properly, there’s a good chance some search engines and AI tools won’t see all of it. This isn’t an argument against JavaScript. It’s simply a reminder that important content should remain easy to discover and understand, regardless of the technology used to deliver it.

How consistency strengthens understanding

Content

Item 1

Text: In most cases, AI doesn’t struggle because a website uses modern technology. It struggles because the information itself is inconsistent, fragmented or lacking context. For example, one page may refer to “undergraduate courses”, another to “degree programmes” and a third simply to “study options”. A person familiar with the organisation will usually understand that these phrases describe similar concepts. AI attempts to make those same connections, but inconsistent terminology can introduce uncertainty, especially when combined with other mixed signals. The same applies to duplicated content, conflicting information, vague page titles, weak heading structures or content that has gradually evolved without a clear strategy. The same principle applies across an entire website. Imagine finding a single page titled “Our Services” with very little supporting information elsewhere on the website. Now compare that with an organisation whose services are explained in depth, linked to relevant case studies, supported by detailed guidance articles and reinforced consistently throughout the website. Both websites may mention the same services, but the second creates a much richer network of information that reinforces what the organisation does and how its expertise fits together. This is one reason websites should continue evolving after launch. As organisations grow, content expands, priorities shift and language evolves. Regularly reviewing structure and content helps maintain clarity for everyone who relies on the website, whether that’s a visitor, a search engine or an AI system.

What about LLMs.txt?

Content

Item 1

Text: You may have seen discussions about LLMs.txt, a proposed web standard designed to help AI systems identify important content on a website. While it’s a promising development, it complements rather than replaces established web standards such as HTML, structured data and XML sitemaps. For now, LLMs.txt should be viewed as a possible addition rather than a replacement for good website structure, clear content and established web standards. Those foundations remain the primary means by which search engines and AI systems discover and understand websites.

What organisations should focus on

Items

Content

Item 1

Text: Understanding how AI interprets websites can make the technology seem much less mysterious. Rather than trying to think like a person, AI gradually develops an understanding by combining many different signals. It considers structure, content, context, relationships and technical implementation together. The clearer and more consistent those signals are, the easier it becomes to interpret what a website is about. For organisations, the practical implications are reassuring. There is no separate checklist for building an “AI website”, just as there is no single feature or optimisation that guarantees AI systems will understand your content. Instead, the focus should remain on the same principles that have shaped well-designed websites for many years. That means creating websites that are: These qualities improve the experience for visitors today while also helping search engines and AI systems interpret information more accurately. They also reinforce the importance of treating a website as something that evolves, rather than something that’s launched and forgotten. As organisations grow, publish new content and introduce new services, the relationships between pages, the consistency of terminology and the overall structure of the site all deserve regular attention. A website that remains accurate, well organised and technically sound is easier for people to use and easier for automated systems to understand. This is also why SEO, accessibility and AI shouldn’t be treated as separate concerns. Clear information architecture, semantic HTML, descriptive content and accessible design have long supported good user experiences and search visibility. Increasingly, those same qualities also help AI interpret websites with greater confidence.

The fundamentals haven’t changed

Content

Item 1

Text: More people are now discovering information through AI-powered search experiences alongside traditional search engines. As those tools become more common, organisations are beginning to think differently about how their content is found. What hasn’t changed is the importance of building websites that communicate clearly. In many ways, this isn’t the first significant shift the web has experienced. In the early years of the internet, it wasn’t unusual for websites to be designed differently for different browsers. Some organisations even recommended that visitors use Internet Explorer or Netscape Navigator because there was no guarantee every browser would display a page in the same way. As web standards matured, that approach gradually disappeared. Rather than building separate websites for different browsers, organisations benefited from creating one well-structured website that worked consistently for everyone. AI represents a different kind of evolution, but the lesson is surprisingly similar. The technology will continue to evolve. New AI models will emerge, search experiences will develop and different platforms will interpret information in different ways. The principles behind good websites are unlikely to change as quickly. Organisations don’t need separate websites for AI. They need websites that communicate clearly, organise information logically and are built on accessible, well-maintained technical foundations. Those qualities have always helped people understand what an organisation does. Increasingly, they’re helping AI do the same.

AI search & website visibility: a complete guide

Parent id: the-fundamentals-havent-changed

Parent heading: The fundamentals haven’t changed

Content

Item 1

Text: Discover how AI Search is changing website visibility, how AI systems understand websites and the... 15 minutes read Read the guide

Entity SEO Explained: How AI Understands Organisations

Parent id: the-fundamentals-havent-changed

Parent heading: The fundamentals haven’t changed

Content

Item 1

Text: Learn what Entity SEO is, how AI understands organisations through entities and relationships and why... 18 minutes read Read the guide

How AI Search is Changing SEO

Parent id: the-fundamentals-havent-changed

Parent heading: The fundamentals haven’t changed

Content

Item 1

Text: Learn how AI Search is changing SEO, what still matters, where the emphasis is changing... 16 minutes read Read the guide

What Is AI Search and How Does It Work?

Parent id: the-fundamentals-havent-changed

Parent heading: The fundamentals haven’t changed

Content

Item 1

Text: Building trust online starts long before someone gets in touch. Learn how your website can... 12 minutes read Read the guide

What is Structured Data? How Search Engines and AI Understand Your Website

Parent id: the-fundamentals-havent-changed

Parent heading: The fundamentals haven’t changed

Content

Item 1

Text: Learn what structured data is, how Schema.org and JSON-LD work, and why structured data helps... 19 minutes read Read the guide

Zero-Click SEO: A Complete Guide

Parent id: the-fundamentals-havent-changed

Parent heading: The fundamentals haven’t changed

Key points

Content

Item 1

Text: Wondering why your website is getting fewer search clicks? Learn what zero-click SEO is, how... 15 minutes read Read the guide

Entities

Information architecture

Structured data

Google AI Mode

Search engines

Accessibility

Semantic HTML

Perplexity

AI systems

JavaScript

DeepMind

LLMs.txt

ChatGPT

Concept relationships

Item 1

Subject: Clear information architecture

Predicate: supports

Object: AI interpretation of websites

Item 2

Subject: Alternative text

Predicate: helps

Object: AI and other systems understand image content

Item 3

Subject: Semantic HTML

Predicate: contributes to

Object: user experience and search visibility

Item 4

Subject: Semantic HTML

Predicate: provides

Object: clues about page organisation

Item 5

Subject: Consistent terminology

Predicate: reinforces

Object: AI understanding

Item 6

Subject: Structured data

Predicate: provides

Object: additional context

Item 7

Subject: Accessibility

Predicate: supports

Object: AI interpretation

Item 8

Subject: Crawlers

Predicate: discover

Object: webpages

Implementation patterns

Item 1

Pattern: Maintain a well-structured website with consistent terminology

Description: Ensure that content is logically organised and uses consistent terms across pages to help AI systems understand entities and relationships.

Benefit: Improves clarity for users and automated systems

Item 2

Pattern: Semantic HTML markup

Description: Using HTML elements to describe what content is, not just how it looks, giving AI and assistive technologies clearer clues about page organisation.

Benefit: Improves both accessibility and AI understanding of page structure.

Item 3

Pattern: Logical information architecture

Description: Organising content into categories and sections with clear navigation, breadcrumbs, and internal links.

Benefit: Helps AI understand how topics connect and establishes areas of expertise.

Item 4

Pattern: Semantic HTML

Description: Using semantic HTML elements to structure content clearly, helping AI understand the hierarchy and meaning of different parts of a page.

Benefit: Provides a framework that AI can interpret more accurately.

Item 5

Pattern: Create a rich network of linked content

Description: Explain services in depth, link to case studies, and reinforce with guidance articles.

Benefit: Builds a richer network of information that reinforces what the organisation does.

Item 6

Pattern: Structured data markup

Description: Adding Schema.org structured data to identify entities and details such as publication dates, authors, or locations.

Benefit: Reinforces clear content and structure with another reliable signal.

Item 7

Pattern: Provide alternative text for images

Description: Write descriptive alt text that conveys the same information a sighted visitor would gain.

Benefit: Gives AI and other systems useful information about image content.

Item 8

Pattern: Use semantic HTML for headings

Description: Mark up headings correctly in HTML to provide additional clues about page organisation.

Benefit: Helps AI systems understand the structure and hierarchy of content.

Practical guidance

Item 1

Guidance: Use semantic HTML to mark up headings, paragraphs, navigation, articles, lists, buttons and footers so that AI systems have clearer clues about the purpose of each element.

Importance: high

Item 2

Guidance: Ensure websites are discoverable by crawlers through following links, XML sitemaps, and revisiting websites to identify new or updated content.

Importance: medium

Item 3

Guidance: Consider that AI systems may need to render pages to understand content not immediately available in initial HTML, so design with that in mind.

Importance: medium

Item 4

Guidance: Ensure headings create a clear hierarchy that helps readers and AI understand the main topic and supporting information.

Importance: high

Item 5

Guidance: Focus on clear writing, logical organisation, accessibility, and meaningful internal links to support AI understanding.

Importance: high

Item 6

Guidance: Implement structured data to provide additional context, but focus on clear content and good website structure first.

Importance: medium

Item 7

Guidance: View LLMs.txt as a possible addition rather than a replacement for good website structure and established standards.

Importance: medium

Item 8

Guidance: Use navigation, categories, breadcrumbs, and internal links to establish relationships between pages and topics.

Importance: high

Risk areas

Item 1

Risk: Over-reliance on structured data without quality content

Impact: Structured data cannot compensate for poorly written or poorly organised content.

Item 2

Risk: Treating SEO, accessibility, and AI as separate concerns

Impact: May lead to fragmented strategies that miss shared benefits

Item 3

Risk: Assuming a single change can make a website AI-friendly

Impact: Leads to neglect of other important signals and overall website quality.

Item 4

Risk: Important content only available after user interaction

Impact: May not be seen by some search engines and AI tools

Item 5

Risk: Neglecting website maintenance after launch

Impact: Reduces accuracy and clarity, making it harder for AI to interpret content

Item 6

Risk: Inconsistent terminology across pages

Impact: Introduces uncertainty for AI systems trying to connect similar concepts

Item 7

Risk: Duplicated or conflicting content

Impact: Weakens the clarity of signals for AI interpretation

Item 8

Risk: Poor information architecture

Impact: Makes it harder for AI to understand how topics connect, reducing the website's visibility in AI-driven search.

Common misconceptions

Item 1

Misconception: AI understands a webpage because of one particular feature, such as headings, structured data, or metadata.

Clarification: AI builds understanding by combining many different signals; no single element tells the whole story.

Item 2

Misconception: There is a single feature or optimisation that guarantees AI systems understand your content.

Clarification: AI understanding is built on multiple enduring principles, not any one technique.

Item 3

Misconception: A single technology or piece of markup is enough for AI to understand a website.

Clarification: AI combines many different pieces of information to build a picture of a website.

Item 4

Misconception: AI understands websites the same way humans do visually.

Clarification: AI builds understanding from multiple signals including HTML structure, headings, links, and metadata, not just visual appearance.

Item 5

Misconception: LLMs.txt is a replacement for established web standards.

Clarification: LLMs.txt complements rather than replaces HTML, structured data and XML sitemaps.

Item 6

Misconception: Structured data tells AI everything about a webpage.

Clarification: Structured data provides additional context but does not replace clear content or good website structure.

Item 7

Misconception: JavaScript is the problem for AI understanding.

Clarification: JavaScript itself isn't usually the issue; problems arise when important content only loads after interaction and isn't easily discoverable.

Item 8

Misconception: Accessibility is an AI optimisation technique.

Clarification: Accessibility is about clear communication with people, but content designed for people often communicates more clearly with machines as well.

Technical context

Crawlers: Automated systems used by search engines to discover webpages by following links, reading XML sitemaps, and revisiting websites.

Semantic html: Correctly marked up headings provide additional clues about page organisation.

Rendering: Process where a system may need to render a page to understand content not immediately available in initial HTML.

Structured data: LLMs.txt complements rather than replaces structured data and XML sitemaps.

Accessibility: Good alternative text helps AI and other systems understand image content.

Information architecture: Navigation, categories, breadcrumbs, internal links

Javascript rendering: Most major search engines can process JavaScript, but AI systems vary in their ability.

Reasoning paths

Item 1

User problem: Misconception that a single feature (e.g., headings, structured data, metadata) makes a website AI-friendly

Why this guide is relevant: This page explains the topic and gives practical points to consider.

Evidence refs

Relevant sections

Item 2

User problem: Understanding how AI interprets websites when users experience them visually

Why this guide is relevant: This page explains the topic and gives practical points to consider.

Evidence refs

Relevant sections

Item 3

User problem: Communicating content purpose to automated systems without proper markup

Why this guide is relevant: This page explains the topic and gives practical points to consider.

Evidence refs

Relevant sections

Item 4

User problem: Lack of clear information architecture leading to poor AI understanding

Why this guide is relevant: This page explains the topic and gives practical points to consider.

Evidence refs

Relevant sections

Knowledge hub

Page type: guide

Learning intent: AI systems understand websites by combining multiple signals including visible content, HTML structure, headings, internal links, metadata, accessibility, and relationships between content, rather than relying on a single technology or piece of markup.

Topic hierarchy

Primary: How AI systems understand websites through enduring web principles: clear communication, logical structure, accessibility, and technical soundness.

Supporting

Evergreen use: true

Content profile

Content type: Knowledge Hub guide

Primary topic: How AI systems understand websites through enduring web principles: clear communication, logical structure, accessibility, and technical soundness.

Supporting topics

Primary audience

Retrieval topics

Organisation

Name: Feel Created

Type: Digital partner specialising in websites and digital platforms

Url: https://www.feelcreated.com/

Service model: We work as an extension of our clients' teams, combining strategy, UX, accessibility, content design, WordPress development and ongoing support to create digital platforms that help organisations communicate more clearly, work more efficiently and grow sustainably.