Hard-to-parse pages
Key service details appear only after interaction or inside images. We compare delivered and rendered HTML for important URLs.
LLM SEO
LLM SEO: can systems access and understand your content?
We check specific URLs to see whether important information is present in delivered HTML, remains accessible and has a clear structure. LLM SEO extends technical SEO by asking whether offers and terms are easy to find and understand in AI search.
Discuss your project ↗︎When is LLM SEO useful?
LLM SEO is useful when AI-powered search systems cannot reliably access key information or interpret its structure.
Fragmented knowledge
Product features, conditions and answers are spread across pages without clear connections. We review structure, internal links and canonical destination pages.
Unclear terminology
The same offer has different names or ambiguous abbreviations. We document where wording and page structure may lead to misunderstandings.
What findings will the LLM accessibility review provide?
We document testable findings rather than speculative “LLM ranking factors”.
| Deliverable | What you receive |
|---|---|
| URL and HTML findings | Example pages with status codes, indexing signals, visible HTML and relevant structure issues. |
| Prioritised implementation tickets | Specific requirements for developers and editors, including affected URLs and acceptance checks. |
| Follow-up validation | A check of agreed changes on staging or live pages to confirm the content is actually accessible. |
Technical foundations for LLM SEO and ChatGPT SEO
We review technical accessibility, clearly identified services and the structure of important information for AI-powered search systems.
Accessibility
We review robots.txt, status codes and potential CDN or firewall blocks for search crawlers and user-initiated requests, including OAI-SearchBot, ChatGPT-User, PerplexityBot, Claude-SearchBot and Claude-User. We assess model-training controls separately: GPTBot and ClaudeBot serve different purposes from search crawlers. Google-Extended and Applebot-Extended are additional usage controls, not switches for general search visibility.
Terms and entities
We examine Terms and entities in relation to your goals and derive practical checks and recommendations.
Structured content
We examine Structured content in relation to your goals and derive practical checks and recommendations.
Quality review
We examine Quality review in relation to your goals and derive practical checks and recommendations.
Mentions and answers vary by system, question and time. We document observations; inclusion or citation cannot be guaranteed.
Common questions about LLM SEO and AI crawlers
How is LLM SEO different from technical SEO?
The technical foundation overlaps. Here we also check whether key claims, terms and relationships are easy to find and understand on the page.
Do I need an llms.txt file?
An llms.txt file cannot replace accessible web pages and does not guarantee AI citations. We check HTML, internal links and crawling signals first. My practical test on llms.txt and traffic, linked below, explains the observations and limits of that analysis.
Does structured data guarantee AI mentions?
No. Appropriate markup can clarify content, but it must match what is visible on the page and cannot guarantee a mention.
What does an LLM SEO review cost?
The scope depends on page types, technology and depth of review. Once defined, you receive a proposal with clear URLs and deliverables.
What would you like to change?
Briefly describe your starting point. Together we identify a useful next step.