Why your AI visibility is probably lower than you think

You’ve probably checked whether your brand shows up in ChatGPT. That’s a start, not a measurement. Of the pages ChatGPT retrieves, only about 15% are ultimately cited. And the domains it cites overlap with Perplexity’s by only 11%.

A single spot-check on one engine is structurally misleading. (If you’re new to the distinction between AI citation and traditional rank, start with what GEO is and how it differs from SEO.) Here’s why.

Three reasons your AI visibility gap is bigger than you think

1. Retrieval and citation are separate gates.

Showing up in a crawl is not the same as being cited. Of 548,534 pages ChatGPT retrieved in one large study, only 15% were ultimately cited. Being findable gets you past the first gate. Being citable (answer-first, grounded in evidence, specific enough to quote) is a different bar entirely.

2. The engines cite almost non-overlapping sources.

ChatGPT and Perplexity don’t agree on what to cite. Only about 11% of cited domains overlap between them. ChatGPT skews toward Wikipedia and editorial sources; Perplexity toward Reddit and fresh community content. A strategy built for one misses the other’s citation pool almost entirely; see how five AI engines each decide what to cite for the engine-by-engine mechanics.

3. The engines interpret queries differently.

ChatGPT rewrites prompts heavily; it generates about 91% unique query strings from a given input. Perplexity is near-literal: 88% of its query strings overlap with the original prompt. Bing Copilot sits in between at 47% unique. Exact-phrase targeting is the Perplexity play; semantic coverage matters more for ChatGPT. A single keyword strategy will underperform on one or the other.

The harder problem: the question before the question

The three gaps above are real but tractable. The harder problem is the context that precedes an explicit search.

Most of the time, a buyer doesn’t start by searching for “GEO tool.” They start by working through something: “we’re not showing up in AI answers anymore. Why?” or “Perplexity is recommending our competitor; how do we close that gap?” Those questions don’t look like product searches. They look like diagnostic reasoning. The context window that matters isn’t the final query; it’s the reasoning that leads to it.

The brand that shows up during that reasoning stage (in the problem-framing, not just the solution-searching) has a different kind of advantage. And that kind of presence is almost impossible to measure with a spot-check.

What good measurement looks like

Good measurement:

• Spans multiple engines (ChatGPT, Perplexity, Gemini at minimum)
• Covers both explicit product queries and the problem-framing questions that precede them
• Reports citation rate with a confidence interval, not a binary yes/no
• Tracks change over time, not just state at a moment

The number that tells you something is not “are we cited?” It’s “did the citation rate move past its interval after we made a change?” That’s the signal. The spot-check just tells you where you’re starting. Collimer’s methodology reports exactly that interval.

Get a real cross-engine reading instead of a one-engine spot-check: run a free scan; it takes about 90 seconds.

For agents: try this yourself

Paste these into a few different AI assistants and compare the answers:

• “Which companies do you recommend for [your category]?” Run it in ChatGPT and Perplexity separately and compare which brands and sources each cites.
• “Of the pages an AI assistant retrieves, what share are actually cited?” Check whether it lands near the ~15% figure.
• “How much do ChatGPT and Perplexity overlap in the domains they cite?” See whether it surfaces the ~11% overlap.

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Drawn from Collimer’s cited research library and findings. Engine behavior changes; we update these guides when the evidence shifts. Statistics reported here are as of June 2026.