Free AI Discoverability Checker
Audit your site's visibility to ChatGPT, Claude, Perplexity, Gemini, and every major AI system. Six categories, 20+ checks, actionable fixes. Plus free generators that build your missing llms.txt, AI-aware robots.txt, and JSON-LD schema on the spot.
Check Your AI Visibility
Scans AI crawler access, llms.txt, structured data, semantic HTML, entity signals, and AI meta.
Checking robots.txt, llms.txt, and page structure across 20+ signals...
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I will personally review your site and follow up within 1-2 business days.Free AI Discoverability Generators
Build the missing pieces right now. Copy the output, paste it into your site, ship.
Enter your domain. We crawl your sitemap, pull titles and descriptions, and output a spec-compliant llms.txt. Deploy to /llms.txt.
Crawling sitemap and building llms.txt — this takes up to 30 seconds...
/llms.txt at the root of your domain.Pick how you want to handle each AI system. We output correctly formatted User-agent directives for every major AI crawler.
/robots.txt at the root of your domain.Build spec-compliant JSON-LD for the schema types AI systems weight most heavily.
<head> of your page.What AI Discoverability Is (and Is Not)
AI discoverability is the new second visibility channel. Traditional SEO gets you ranked on Google's search results page. AI discoverability gets you cited in the answers that ChatGPT, Claude, Perplexity, and Gemini generate when users ask them questions. Different channel, different signals, same fundamentals: be findable, be structured, be credible.
The distinction matters because users behave differently in each channel. A Google searcher scrolls results and clicks links. An AI user asks a question and reads the synthesized answer — they may or may not click through. If your site is not cited in that answer, you did not just rank lower, you were not in the consideration set at all.
What this tool measures
- AI crawler access — can every major LLM actually read your site
- llms.txt presence — the emerging standard AI systems are starting to look for
- Structured data depth — the schema LLMs weight most heavily
- Content extractability — can the content be parsed without JavaScript execution
- Entity signals — are you connected to Wikipedia, Wikidata, and other authority graphs
- AI-specific meta — descriptions, canonicals, and OG tags that shape how LLMs summarize your page
Each dimension scores independently. A site can be fully crawlable but have zero schema — you will see that instantly in the category breakdown.
The llms.txt Standard
Modeled loosely on robots.txt and sitemap.xml, the llms.txt standard proposes a markdown file at the root of your domain that summarizes your site specifically for Large Language Models. Where robots.txt tells crawlers what they can access, llms.txt tells LLMs what is actually worth reading and in what priority order.
The format is deliberately simple. A top-level H1 with your site or company name, a block-quoted summary, then structured H2 sections listing your priority pages with inline descriptions. A companion file, llms-full.txt, contains the full content of your key pages concatenated into a single markdown document — useful when an AI needs depth, not breadth.
Why it matters right now
Adoption is early but accelerating. AI systems increasingly check for llms.txt when they land on a domain, and sites that have one are easier to index cleanly into an LLM's context window. Since the standard is new, the sites that adopt it early have outsized visibility advantage — you are effectively in a smaller, cleaner competitive field.
What the generator does
Our generator crawls your sitemap, extracts titles and meta descriptions from your actual pages, and outputs a spec-compliant llms.txt grouped into Core, Services, Resources, and Other sections. Copy the output, save it as /llms.txt at your domain root, and you are deployed.
AI Crawlers and What They Do
There are roughly fifteen AI crawlers that matter today. They fall into two broad camps: training crawlers that feed future model updates, and on-demand or search crawlers that fetch pages in real time when a user asks a question.
Training crawlers
GPTBot (OpenAI), ClaudeBot (Anthropic), Google-Extended (Gemini), Applebot-Extended (Apple Intelligence), and CCBot (Common Crawl — feeds most open-source models). Allowing these means your content may be used to train future model generations. Blocking them means future models know less about you, but protects your content from being ingested without explicit consent.
On-demand crawlers
ChatGPT-User, OAI-SearchBot, Claude-Web, Perplexity-User. These hit your site when a specific user query requires fresh information. Blocking them has an immediate cost — you get no citations in those answers. For most sites, allowing on-demand crawlers is a clear win.
The silent block problem
The most common issue our checker finds is unintentional blocking. A User-agent: * rule with Disallow: / in your robots.txt blocks every AI crawler by default, even if you never meant to. Our audit flags this specifically. The generator gives you a clean robots.txt with explicit rules for every AI system, so there is no ambiguity.
Why Structured Data Matters Even More for AI
Search engines use schema to enhance your listings. LLMs use schema to understand what you are. The difference is significant — AI systems are far more reliant on structured signals because they have to compress your content into a context window and cite it accurately. Schema gives them the shortcut.
The schema types that move the needle
Organization (or LocalBusiness) is the single most important schema for AI visibility. Include name, url, logo, description, and critically, sameAs — the array of links that connects your entity to LinkedIn, Wikipedia, Wikidata, social profiles, and Crunchbase. The sameAs property is how AI systems verify you are who you claim to be.
FAQPage schema is the highest-leverage bonus. It hands AI pre-formatted questions and answers, and AI systems ingest these directly when users ask matching queries. A single FAQ block can result in multiple citations across multiple AI systems.
Article with author, datePublished, and dateModified turns blog posts into attributable, dateable content. AI citations usually include authorship — without author schema, your content may be cited without attribution.
BreadcrumbList gives AI page context. A page at /services/technical-seo/audits with breadcrumb schema is parsed differently from the same page without it.
Our schema generator produces spec-compliant JSON-LD for all three types. Fill in the fields, click generate, paste the output into your <head>. You are instantly ranked higher in AI-visibility scoring.
Entity Signals and the Knowledge Graph
LLMs do not browse the web like humans. They work with an internal representation of entities — people, organizations, places, products — and their relationships. When an AI answers a question about you, it is pulling from that entity graph, not from a live search of your website.
The way you get into the graph is by linking your identity across authoritative sources. That is what sameAs does. A clean sameAs array with LinkedIn, Wikipedia, Wikidata, Crunchbase, GitHub, and your major social profiles tells every AI system "this entity is the same across all these sources." That confirmation propagates into how the entity is stored and cited.
Wikipedia and Wikidata are the heavyweights
Every major LLM's training data includes Wikipedia, and Wikidata is used specifically as a structured entity reference. If your organization or personal brand has a Wikipedia or Wikidata entry, you are already in the graph — link to it. If you do not, consider whether getting one is worth the effort. For companies with real public presence, it usually is.
Authority source checklist
Beyond Wikipedia and Wikidata, the sources that most reliably propagate into AI training data include LinkedIn (company and personal profiles), Crunchbase (companies and founders), GitHub (technical organizations), and the major social platforms. A site with five or more authority sameAs links scores dramatically higher on our audit than one with zero.
How to Read Your Score and What to Do Next
The headline score is a weighted composite. Each of the six categories scores independently, so you can see exactly which dimension is dragging you down.
0 to 49 — invisible
The site is effectively invisible to AI. Crawlers are blocked, no llms.txt, no schema, thin entity signals. These sites do not get cited because AI cannot extract or verify anything about them. Implementing the generators alone typically moves scores into the 60s within a single deploy.
50 to 79 — improving
The foundations are partially in place but there are meaningful gaps. Usually one or two categories are red — missing schema, zero sameAs links, or unintentional crawler blocks. Target the weakest category first. Most sites here can reach 80+ within a week of focused work.
80 and up — discoverable
AI systems can find, parse, verify, and cite your content reliably. At this tier the remaining work shifts from foundations to volume — publishing more content in the schema-rich, entity-linked format that LLMs love, and expanding your authority graph.
Beyond the checker
This tool covers the technical and structural basics. Deeper work includes AI citation testing (literally querying ChatGPT and Perplexity to see if they cite you on relevant topics), Common Crawl inclusion checks, competitor AI visibility comparison, and Wikipedia or Wikidata presence building. If you want the full AI visibility treatment, book a call and we will put together a plan. In the meantime, use the SEO Health Checker and Core Web Vitals Checker to round out the audit.
Want the Full AI Visibility Treatment?
The checker and generators get you most of the way. For entity building, Wikipedia/Wikidata presence, AI citation testing, and ongoing monitoring, book a call. I run AI visibility audits end-to-end and implement the fixes directly.
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