Technical GEO is about being reachable and readable for AI retrieval systems: 122 of 600 audited sites are partly invisible because bot protection blocks AI crawlers, schema shows no measurable citation effect, and llms.txt files go unread 97% of the time.
This hub collects GeoXylia's technical GEO cluster: AI crawler governance, schema markup, llms.txt, rendering, and the other infrastructure questions that decide whether AI systems can read your site at all. The theme across the cluster is honesty about what is a lever and what is infrastructure. Two once-hyped tactics, llms.txt and schema-as-citation-magic, fail the evidence test. One thing does not: if AI crawlers cannot reach your page, nothing else you do matters.
Who is this for? Engineers, technical SEOs, and the marketers who translate their work. The distinction that runs through every guide here is retrieval versus training: retrieval bots (OAI-SearchBot, ChatGPT-User, PerplexityBot, Claude-SearchBot, Google-Extended) fetch pages to answer live questions, while training bots (GPTBot, ClaudeBot, CCBot) collect data for model training. You can block the second group freely; blocking the first costs you citations.
Is llms.txt worth my time?
Keep expectations low. Ahrefs found 97% of llms.txt files are never read by AI crawlers, adoption sits at 10.13% of domains with no meaningful traffic-tier difference (SE Ranking), and no major AI platform officially supports the format. The honest framing: llms.txt is infrastructure, not a lever. It costs almost nothing to publish and it functions as a content map for AI coding assistants and developer tools, which is a real but narrow audience. It is not crawl control like robots.txt, and it will not move your AI search citations. If you publish one, keep it accurate and short, and spend your optimization effort where the evidence is: extractable content, entity signals, and crawler access. The guides in this cluster show exactly how to do each of those.
Does schema markup get me more AI citations?
No measurable effect on already-cited pages. Ahrefs ran a 1,885-page quasi-experiment adding JSON-LD schema to pages and measured AI Overviews at -4.6%, AI Mode at +2.4%, and ChatGPT at +2.2%, all within noise. Studies that claim schema lifts citations are observational, and the correlation runs the other way: well-optimized sites tend to have both schema and citations. The correct frame is that schema is necessary infrastructure. Organization with a full sameAs array, Article with author and dateModified, and BreadcrumbList give AI systems the machine-readable context they need to recognize your entity and your content's provenance. One nuance remains: schema may help pages with no citations at all cross an eligibility threshold, but that effect is plausible, not proven, while on already-cited pages the measured effect is noise. Keep schema valid, complete, and matching what users see. Just do not expect it to produce citations by itself; visible, answer-shaped text is what AI extracts.
Which AI crawlers can reach my site?
In GeoXylia's 476-site audit, 122 of 600 sites, about 20%, had to be excluded because bot protection blocked our AI-based checks. That is the scale of the problem: roughly one site in five is partially invisible to AI systems, often without the owner knowing. The fix is a deliberate robots.txt policy. Allow the retrieval bots that feed live answers: OAI-SearchBot, ChatGPT-User, PerplexityBot, Claude-SearchBot, and Google-Extended. Decide separately on training bots: GPTBot, ClaudeBot, and CCBot can be blocked without losing citations. One published test found citations dropped 85% after a site blocked retrieval bots, so the allowance check belongs in every technical audit. Remember that AI crawlers do not execute JavaScript, so server-side or static rendering is the baseline; content injected by scripts is never retrieved. Audit your robots.txt, your hosting rules, and your rendering before you touch content.
Where to start
Start with the pillar guide, Technical SEO for AI Search: The Complete Implementation Guide, then read the llms.txt setup guide and the image SEO guide for the visual channel. Check your own crawler access and technical foundation with the free audit at geoxylia.com/audit.
Sources: Ahrefs: schema quasi-experiment · SE Ranking: llms.txt analysis · llmstxt.org · Google Search Central: robots.txt
Disclosure: AI-assisted, human-edited. Statistics verified against the GeoXylia research base.