Technical SEO for AI search: the complete infrastructure checklist

The four layers of technical SEO for AI

Traditional technical SEO ensures search engines can crawl and index your pages. Technical SEO for AI adds four requirements: discoverability (can AI crawlers find you?), accessibility (can they read the content?), parseability (can they extract structured answers?), and trustability (do your technical signals indicate a reliable source?).

• Discoverability — robots.txt AI crawler rules, sitemap.xml, llms.txt, IndexNow submissions.
• Accessibility — server-side rendering (no JS-dependent content), fast response times, no crawler-blocking firewalls.
• Parseability — JSON-LD structured data, clean HTML with semantic headings, answer-first content structure.
• Trustability — HTTPS, proper canonical URLs, consistent entity data (Organization schema matches your About page).

AI crawler management

AI crawlers identify themselves by user-agent string. The major ones you should explicitly allow in robots.txt: GPTBot and OAI-SearchBot (OpenAI/ChatGPT), PerplexityBot (Perplexity AI), ClaudeBot (Anthropic/Claude), Google-Extended (Google Gemini/AI Overviews), Bingbot (Microsoft Copilot), and Applebot-Extended (Apple Intelligence).

The critical mistake many sites make: broad 'Disallow' rules that accidentally block AI bots. A blanket 'Disallow: /' for all unknown user agents blocks every AI crawler. Instead, use a wildcard 'Allow' for all, then name the AI crawlers explicitly as a signal of intent.

Server-side rendering is non-negotiable

Research from Semrush's 2026 GEO guide confirms that AI crawlers have trouble executing JavaScript. If your content relies on client-side rendering (React SPA without SSR, for example), AI crawlers see an empty page with a loading spinner. Server-side rendering (SSR) or static site generation (SSG) ensures your content is in the HTML when the crawler arrives.

This is one of the biggest technical differences between SEO for traditional search and SEO for AI search. Googlebot can execute JavaScript (albeit slowly); most AI crawlers cannot or do not.

Structured data implementation

JSON-LD is the only structured data format you should use for AI search. Microdata and RDFa are technically valid but harder for AI systems to parse reliably. Every content page should have at minimum:

• Article schema — headline, author (Person with jobTitle), datePublished, dateModified, publisher (Organization).
• FAQPage schema — if the page has FAQ sections, wrap them in FAQPage/Question/Answer markup.
• BreadcrumbList — tells engines where the page sits in your site hierarchy.
• Organization schema — on the homepage, with name, url, logo, sameAs (social profiles), and description.
• Product/Offer schema — on pricing and product pages, with price, currency, and availability.

The llms.txt discovery file

llms.txt is an emerging standard (llmstxt.org) that serves as a directory of your most important content for AI engines. It lists your pages with descriptions, organized by topic, so AI crawlers can prioritise what to read. Think of it as a curated sitemap specifically for AI consumption.

A companion llms-full.txt contains the full article text (not just URLs and descriptions), giving AI engines deep grounding material without requiring them to crawl and parse each page individually. Fastlook generates both files automatically from your published content.