Free AI Content Visibility Checker
Enter a URL and get a 0–100 score for how ready your page content is for large language models — whether ChatGPT, Claude, Gemini and Perplexity can fetch it, parse it, and use it when they answer.
AI Content Visibility Checker
Analyse whether your page content is optimised for LLMs like ChatGPT, Claude and Gemini.
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How to check if your content is ready for LLMs
One URL, no account. The crawler fetches your page the way an AI crawler does and scores what the model can actually see.
Enter any URL
Your homepage is the usual starting point, but any page works. Check a competitor’s page the same way — nothing here requires you to own the domain.
We fetch it the way an LLM crawler does
The crawler requests the page without executing your JavaScript, because most AI crawlers do not execute it either. Whatever is missing from that response is content the models never see — and cannot “rank” or cite.
Read your content score and the fix list
You get an overall 0–100 content visibility score, a pass or fail on each check, and fixes ordered by how much each one is costing your LLM readiness.
What AI content visibility actually means
This checker does not track mentions. It asks whether your content is technically fit for LLMs to read. These six checks are that half of Generative Engine Optimization.
AI content visibility score
A single 0–100 figure for how readable this URL is to AI crawlers and models. It measures content readiness — not how often you are already mentioned or cited in answers.
JavaScript-hidden content
Most AI crawlers do not run JavaScript. If your copy is injected client-side, the models receive an empty shell — a strong page for humans and a blank one for ChatGPT. This is the single most common failure.
llms.txt detection
llms.txt is a proposed plain-text file at the root of a domain that tells language models which pages matter and how to describe them. It is the robots.txt idea applied to AI. The check reports whether yours exists and parses.
Structured data
Schema.org markup — Organization, LocalBusiness, FAQPage, Article — states in machine-readable form what a page would otherwise only imply. It is how a model knows your opening hours are opening hours and not decoration.
Language and hreflang
The HTML lang attribute and hreflang annotations tell a model which language a page is in and which other versions exist. Get them wrong and it can answer a Spanish question with your English page, or skip you entirely.
Local AI signals
An embedded map, a clickable tel: link and a consistent postal address are what let an assistant answer “who does this near me” with your business. Without them a local company is invisible to exactly the queries it should win.
If LLMs cannot read the page, they cannot put you in the answer
Google renders JavaScript, tolerates thin markup and has years of signals about your site. LLMs fetching a URL have almost none of that. They request the page, read what comes back, and either understand your content or move on to a competitor that was easier to parse.
That is what this free AI content visibility checker measures. It is not the Free AI Visibility Checker next door — that one suggests prompts and opens brand mention tracking. This one scores whether your content is fit for models to read at all.
- Pass or fail per content check, with no interpretation required
- Fixes ordered by how much they hurt LLM readability
- Works on any URL, including a competitor’s
- Re-run it after a deploy to confirm the fix landed
The four failures we see most
- Body copy rendered client-side, invisible without JavaScript
- No Organization schema, so the model cannot name the company
- A missing or wrong lang attribute on a multilingual site
- A local business with no address in the HTML at all
Share the content score, or put your own logo on the report
Every report gets a public link you can send to a client without asking them to create an account, and a PDF export for the ones who still want an attachment.
On a paid plan the same report carries your brand instead of ours — which matters when “is our content ready for AI?” is the deliverable you are charging for.
- A public link that needs no login
- PDF export for the deck or the invoice
- White-label branding on paid plans
- A repeatable content score to show progress between engagements
What clients actually ask
- “Can ChatGPT even read our pages?” — this gives you a number
- “What is blocking the content?” — this gives you the list
- “Did the fix help?” — re-run it and compare
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AI content visibility, answered
What the checker looks at, what the score means, and how it differs from brand tracking.
What is the AI Content Visibility Checker?
It is a free tool that checks whether the content on a URL is optimal for LLMs to read and use. It fetches the page the way an AI crawler does, scores technical readability (GEO), and lists what to fix so models can actually see your copy.
How is this different from the Free AI Visibility Checker?
This AI Content Visibility Checker scores page content readiness for LLMs — JavaScript reachability, schema, llms.txt, language and local signals. The Free AI Visibility Checker suggests prompts for your brand and leads into mention, citation and share-of-voice tracking. One asks “can they read you?”; the other asks “which questions should you track?”
How is the AI content visibility score calculated?
It combines every individual check on the page — content reachable without JavaScript, structured data, llms.txt, language annotations and local signals — weighted by how much each one affects whether a model can use your content. It is a measure of technical content readiness, not of how often you are currently mentioned.
What is an llms.txt file and why does it matter?
llms.txt is a proposed plain-text file at the root of your domain that tells language models which pages matter and how to summarise them, in the way robots.txt tells crawlers where to go. Support is still emerging, so a missing one costs you little today — but it is a few minutes of work for a signal that is only going to count for more.
Why does structured data matter to an LLM?
Schema markup removes guesswork. Without it, a model has to infer from prose that a string of digits is a phone number or that a company is a business rather than a topic. With it, that is stated. Models prefer sources they can be confident they have understood correctly.
Why do you check for maps and clickable phone links?
They are the strongest signals that a page describes a real business at a real place, which is what an assistant needs before recommending you for a “near me” question. A local business with no address in the HTML cannot win those answers.
Does a good score mean ChatGPT will cite me?
No, and it would be dishonest to claim otherwise. A good score means the mechanical obstacles are gone — the models can reach your content and understand what it says. Whether they choose to cite you also depends on your authority, your coverage of the topic and who else is competing for it. Tracking those citations is a different tool.
Can I check a competitor’s site?
Yes. The checker takes any public URL. Running it against two or three competitors is the fastest way to see whether their edge in AI answers comes from stronger content coverage or simply from pages that are easier for a crawler to read.
Can I share or export the report?
Yes. Every report has a public link that needs no account, and a PDF export. Paid plans replace TrueRanker’s branding with your own.
Make your content readable to LLMs — then track who cites it.
Fix the page score here, then follow mentions across AI engines. 14-day trial, no card.