$ ~/ym8 --define technical-aeo
Technical AEO
definition
Technical AEO is the foundational layer of any AI Engine Optimisation strategy. Just as Technical SEO ensures search engine crawlers can discover and index your pages, Technical AEO ensures AI crawlers and language models can effectively process and cite your content.
The core components of Technical AEO include: AI crawler access policies (configuring robots.txt to allow GPTBot, ClaudeBot, PerplexityBot, and other AI crawlers), structured data implementation (Schema.org markup that provides machine-readable context), llms.txt and llm-profile.json files (direct AI-readable brand information), and content architecture optimised for AI extraction (clear headings, concise paragraphs, answer-first formatting).
A Technical AEO audit evaluates each of these components systematically. Common issues include: blocking AI crawlers in robots.txt (either intentionally or inadvertently), missing structured data that would help AI engines understand content context, absent llms.txt files, and content formatting that makes it difficult for AI models to extract clean, citable statements.
Unlike content-focused AEO, which requires ongoing content creation and optimisation, Technical AEO is largely a one-time setup with periodic maintenance. Getting the technical foundation right enables all subsequent content and strategy work to be more effective.
why_it_matters
Without proper Technical AEO, even the best content will struggle to surface in AI-generated responses. Technical AEO ensures that the infrastructure supporting your content is optimised for AI discovery and citation. It is the foundation upon which all other AEO activities are built.
examples
- Auditing robots.txt to ensure AI crawlers like GPTBot and ClaudeBot are not blocked
- Implementing schema markup for FAQ, HowTo, and Product types to aid AI content extraction
- Creating llms.txt and llm-profile.json files with structured brand information
- Restructuring content with answer-first formatting and clear heading hierarchies
faq
What does a Technical AEO audit cover?
A Technical AEO audit covers: AI crawler access policies (robots.txt configuration), structured data implementation, llms.txt and llm-profile.json presence and quality, content architecture and formatting, site speed and accessibility for AI crawlers, and cross-reference checks across AI-discoverable files.
How is Technical AEO different from Technical SEO?
Technical SEO focuses on search engine crawlers (Googlebot, Bingbot) and indexation. Technical AEO extends this to AI-specific crawlers and language model consumption. While there is overlap (both need good site structure and fast loading), Technical AEO adds AI-specific elements like llms.txt, AI crawler policies, and content formatted for AI extraction.
Related Terms
AI Crawlers
AI Crawlers are automated bots operated by AI companies that scan websites to collect content for training data and real-time retrieval. Major AI crawlers include GPTBot (OpenAI), ClaudeBot (Anthropic), PerplexityBot (Perplexity), Google-Extended (Google), and Bingbot (Microsoft).
llms.txt
llms.txt is a plain-text file placed at a website's root that provides structured, machine-readable information about a brand, product, or organisation specifically for consumption by large language models. It functions as a "robots.txt for AI" — telling AI crawlers what your brand is and how it should be described.
Structured Data for AI
Structured Data for AI refers to the use of schema markup (JSON-LD, microdata) and AI-specific files (llms.txt, llm-profile.json) to provide machine-readable context about your content, products, and brand to both search engines and AI engines.
llm-profile.json
llm-profile.json is a JSON-LD structured data file placed at .well-known/llm-profile.json that provides machine-readable brand identity, offerings, expertise, and preferred citation formats to AI crawlers and language models.
Related Engines
Monitor Your AI Visibility
See how your brand appears with the default core pair. Start with ChatGPT and Claude by default. Expand monitoring only when the workflow needs it.