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How do AI assistants see your site?

Check how your website looks to ChatGPT, Perplexity, Claude and Gemini: structured data (Schema.org), the llms.txt file, AI-crawler access (GPTBot, ClaudeBot, PerplexityBot) and how accurately assistants describe your brand. You get a GEO-readiness score from 0 to 100 and a concrete list of what to fix. Free, no signup.

Free, no signup Schema.org, llms.txt & robots Checked in 4 assistants
Running the GEO-check…
Fetching your pages and reading the markup…
This usually takes a few seconds — we scan your markup and AI files, then ask real assistants about your brand.

How it works

We look at your site the way an AI assistant does: first the machine-readable signals, then whether the assistants can actually find and quote you accurately.

1

Enter your site URL

Your website or a competitor's. No account and no access to the site needed — we read what any AI crawler would read.

A few seconds · no signup
2

Scan markup & AI files

We read your Schema.org types, llms.txt, robots.txt rules for GPTBot/ClaudeBot/PerplexityBot, Open Graph and heading semantics.

Schema.org · llms.txt · robots
3

Ask real assistants

We query ChatGPT, Perplexity, Claude and Gemini about your brand and check whether they cite your site and describe it accurately — or make things up.

Citability · brand accuracy
4

Get a GEO score 0–100

We combine the technical signals and the assistant answers into a single GEO-readiness score, with a prioritised list of exactly what to fix first.

Score 0–100 · fixes
Schema.org structured data llms.txt GPTBot / ClaudeBot / PerplexityBot Citability in ChatGPT, Perplexity, Claude, Gemini Brand accuracy

The GEO-check is a technical readiness audit of your own site for AI assistants — it is not the same as our AI Visibility Check, which measures how often assistants mention your brand and your share of voice. Assistant answers can change over time and between sessions, and crawler behaviour depends on your live robots.txt. The result is indicative and points you to the highest-impact fixes.

GEO vs SEO: why machine-readability wins the answer

Classic SEO optimises for a page of ten blue links, where the user still clicks and reads. Generative engines don't hand back a list — they compose one answer and, at most, cite a few sources. To be that source, your content has to be unambiguous to a machine: explicit entities, clean structured data and facts a model can quote without guessing.

That's what GEO measures. Structured data (Schema.org) tells the model what your pages are about; llms.txt points it to the pages that matter; open robots access lets its crawler in at all; and clear, factual copy is what gets quoted accurately instead of paraphrased wrong.

  • Schema.org markup makes entities and facts machine-readable
  • llms.txt gives assistants a curated map of your key pages
  • Blocking GPTBot/ClaudeBot in robots.txt makes you invisible to AI
  • Clear, factual copy is quoted accurately instead of hallucinated