How do you do GEO on your own site in practice?

We apply our GEO methods to our own website. This guide records one working day: the problems we found, the changes we made and the checks that followed. Every example comes from get-geo.ai.

get-geo.ai was built "by the book" from day one: server-side rendering, 9 languages with hreflang, Schema.org JSON-LD, answer-first structure, llms.txt. What could there possibly be to fix? The audit found a problem you cannot see with your eyes.

The site physically lived on www.get-geo.ai, while every signal — canonical, sitemap, hreflang, JSON-LD, llms.txt — pointed to get-geo.ai (the apex, the domain without a subdomain). Meanwhile, the apex answered with a 308 redirect back to www.

The result was a loop: "the canonical version is the apex" → the apex redirects to www → www says "the canonical version is the apex." A classic Google crawler survives this, though it burns crawl budget. AI fetchers often do not: many of them have a budget of one or two requests per page, and some simply never reach the content through a redirect. For GEO this is critical: a file invisible to the fetcher is a file that does not exist.

The fix: the apex was made the primary domain on the host (Vercel), with www set to a 308 redirect to the apex. One switch — and every signal became consistent: the actual address, canonical, internal links, sitemap, and llms.txt now all point to the same place.

Either the bare domain or www can be the primary address. Choose one, redirect the other to it and make sure the canonical URLs, links and sitemap agree, as crawler access depends on it.

The diagnostics were run by an AI agent, and its checklist included: "verify that the apex A record points to 76.76.21.21." Checking the live DNS showed otherwise: the domain resolves to 216.198.79.1 — Vercel's newer infrastructure, which the host migrated to after the model's training cutoff.

The advice was not harmful — just stale. The model was confidently quoting the past.

Check technical advice from an AI model against the live system. In this case, a DNS query was enough to establish that the suggested address was outdated. Without verification, a plausible answer can still describe an old configuration.

After switching the primary host, the old GSC property (a URL-prefix on https://www.get-geo.ai/) became useless: it covers only www, while the pages were moving to the apex.

The correct configuration is a domain property (get-geo.ai, no protocol, no subdomain): it covers apex, www, http, https, and all subdomains at once. It can only be verified via a DNS TXT record — with one pleasant detail: the google-site-verification token is tied to the account, not to the verification method, so a record added to DNS earlier passed verification instantly. Then — submitting the sitemap under the apex address: https://get-geo.ai/sitemap.xml.

The lesson: a freshly submitted sitemap in GSC often sits with a red "Couldn't fetch" status and zero pages. That is not an error but a placeholder until the first processing pass — it is asynchronous and can take up to a couple of days. Before panicking, check three things: the file opens at its direct URL, every <loc> inside points to the right host, and robots.txt contains a Sitemap: line. If all three hold — just wait.

Our sitemap includes more than pages. It lists llms.txt, llms-full.txt, and every language version of llms-full/{locale} — with lastmod dates and priorities.

Why: AI crawlers (GPTBot, ClaudeBot, PerplexityBot) have been observed reading sitemaps, and explicitly listing the LLM files gives them a direct route to the machine-readable version of the site — without relying on the fetcher knowing the /llms.txt convention.

List the content you want crawlers to discover and use lastmod dates that reflect actual updates.

The convention distinguishes two files: llms.txt is a table of contents with links; llms-full.txt is the full content for systems that do not follow links. Our full version initially contained only the landing page: the guides — our most citable asset — were available only page by page.

What we changed:

Half a day of work, not a single new page of content — and yet:

These fixes make content accessible and consistent across the site. They do not establish that a page will be cited, but they remove problems that can prevent a crawler from reading it.