Does ChatGPT Even Know You Exist? A Simple Test Reveals the Truth

You've optimized your website. Schema markup is in place. Content is structured. But here's the uncomfortable question: Do AI systems actually know your website exists?

Here's what happens when someone asks ChatGPT for a product recommendation in your category: The AI retrieves information from sources it knows and trusts. It synthesizes an answer. It may cite sources.

But if the AI has never seen your content – or hasn't indexed it properly – you're not even in the running. You're invisible. Not ranked low. Not outperformed. Simply not there.

This is fundamentally different from Google SEO. With Google, you can check Search Console. You see which pages are indexed. You know when Googlebot last visited.

With AI systems? Nothing. No dashboard. No crawl reports. No visibility into what they know about you.

The solution comes from an old mining practice. Miners used to bring canaries into coal mines. If the canary stopped singing, danger was near. Simple. Effective. Low-tech.

The Canary Test for AI indexing works the same way: You place a unique, traceable marker on your website. Then you ask AI systems about that marker. If they recognize it, you know they've crawled and processed your content.

The principle is straightforward:

Knowing whether you're indexed isn't just academic. It's the foundation for every AI optimization effort.

Consider this scenario: You've invested in GEO optimization. You've added statistics, citations, and quotes to your content. You've implemented schema markup across hundreds of pages. Months pass. You're still not appearing in AI responses.

The question becomes: Is your content not good enough? Or has it simply never been seen?

Without a Canary Test, you're debugging in the dark. You might spend months optimizing content that was never crawled in the first place. Or you might abandon a strategy that would work – if only the AI systems had found your pages.

The Canary Test gives you a diagnostic starting point. It separates "not indexed" from "indexed but not cited." Two very different problems requiring very different solutions.

Here's where it gets interesting for enterprise websites. You can't just slap "TEST-MARKER-XYZ-123" onto your corporate homepage. That would raise questions.

Professional Canary markers need to be:

The best approach uses identifiers that look like internal reference numbers – because that's exactly what they are.

Schema-only markers live exclusively in your JSON-LD structured data:

These are completely invisible to website visitors. They exist only in the code that AI systems parse.

Semi-visible markers appear in low-traffic areas like legal pages:

Nobody questions internal reference numbers. They look professional because they are professional – they just serve a dual purpose.

After placing your Canary markers and waiting 2-4 weeks for AI systems to crawl your site, you test. You ask each major AI system directly: "What is QMS-2025-DE-7X4?"

The responses fall into four categories:

Found and attributed: The AI knows your marker and names your website as the source. This is the best outcome. You're indexed, and the AI trusts you enough to cite you.

Found but not attributed: The AI recognizes your marker but doesn't mention where it learned about it. You're indexed, but citation attribution needs work.

Not found: The AI has no knowledge of your marker. Either your site hasn't been crawled, or the content wasn't processed into the AI's knowledge base.

Hallucinated: The AI invents an explanation for your marker that has nothing to do with reality. Interesting data point, but not directly actionable.

How fast should AI systems find your Canary? Based on observed patterns:

These aren't official benchmarks – AI providers don't publish crawl schedules. But they align with what we observe across different website types.

Fast discovery indicates that AI systems consider your site worth monitoring. Slow or no discovery suggests you're not on their radar – yet.

For deeper insights, use multiple Canary markers across different page types:

Layer 1: Schema-only Tests whether AI systems process your structured data. If this Canary is found, your JSON-LD markup is being read.

Layer 2: Legal pages Tests whether AI systems crawl low-priority content. Legal pages are often crawled last, if at all.

Layer 3: Main content (FAQ, glossary) Tests whether your primary content is indexed. This is where you want to be found.

Layer 4: Fresh content (blog, news) Tests crawl freshness. How quickly do AI systems pick up new content?

By comparing results across layers, you learn not just whether you're indexed, but how deeply and how quickly.

Here's a finding that surprises many: Different AI systems may know completely different things about you.

ChatGPT might find your Canary while Claude doesn't. Perplexity might cite your source while Gemini draws a blank. This isn't a bug – it's how these systems work.

Each AI system has its own crawling infrastructure, its own update schedule, and its own criteria for what to index. Research shows that the overlap between sources cited by different AI systems can be surprisingly low – sometimes under 25% overlap for the same query.

This means you need to test across multiple systems. Being visible to ChatGPT doesn't guarantee visibility to Perplexity. And as users spread across different AI assistants, coverage across all major systems becomes increasingly important.

Let's be clear about what the Canary Test is and isn't. The Canary Test tells you whether AI systems have seen your content. It doesn't tell you whether they trust it, whether they'll cite it, or whether your content is good enough to be recommended.

Think of it as the first diagnostic question: "Is the patient breathing?" Essential to know, but just the beginning of the examination.

The Canary Test is:

The Canary Test isn't:

Once you've confirmed indexing through the Canary Test, the real work begins. Being indexed is necessary but not sufficient for AI visibility. The path forward follows the Content-Code-Credibility framework:

Code (technical foundation): Your Canary Test results show whether this is working. If you're indexed, your technical setup is probably fine. If not, check crawl directives, JavaScript rendering, and bot access.

Content (what you say): Now that you know AI systems see your content, optimize it for citability. Add statistics. Include source citations. Structure information clearly. Make it easy for AI to extract and synthesize.

Credibility (who you are): The hardest factor to influence. Build earned media mentions. Cultivate reviews on independent platforms. Establish your brand as a recognized entity that AI systems learn to trust.

If you want to implement Canary Testing for your website follow these steps to build it up on your own:

The Canary Test represents something larger: the shift from guessing to measuring in AI search optimization.

For years, SEO professionals have had sophisticated tools to understand Google's behavior. AI search optimization is still in its early days. The tools are primitive. The metrics are unclear. Best practices are still emerging.

The Canary Test is a small step toward bringing measurement discipline to AI visibility. It's not perfect. It's not comprehensive. But it answers a fundamental question: Does the AI know you exist? And that's a starting point worth having.