Audit clients for ChatGPT, Copilot, and Google AI Mode

One buyer question, three engines, three different answers.

Start with the buyer's questions. Before scoping a client, ask where their buyers actually type the question: inside Microsoft 365, on Google's results page, or in a ChatGPT window.

A thread on r/DigitalMarketing in October 2026 made the problem almost physical. A Kyiv agency, TapTap, appeared in 0 of 9 ChatGPT runs on 19 September 2026. After its site was rebuilt around buyer questions, with robots.txt and sitemap fixes, it ranked first in Google's AI Overview on 26 September 2026, seven days after that baseline. The same poster pointed to an arXiv study of 112 startups in which ChatGPT recognised 99.4% of them by name and recommended only 3.3%.

Known. Not recommended. I keep circling that gap.

The rebuild and the crawler fixes happened together, so nobody can say which one moved the answer. The evidence does not settle it, and I will not pretend it does.

And yet the shape is clear enough to touch. One engine was silent. Another, a week later, spoke the name first. An audit that checked only ChatGPT on the nineteenth would have reported a company that did not exist, in the same week Google was about to name it.

How do ChatGPT, Copilot and Google AI Mode retrieve sources differently?

Each engine walks a different road to the same question. ChatGPT rewrites the query and searches partly through Bing, Copilot sits on Microsoft's stack, and Google AI Mode fans out across Google's index.

Before trusting any single result, check three things:

1. Which retrieval path the engine uses. Bing sits behind part of ChatGPT search and behind Copilot; Google's own index sits behind AI Overviews and AI Mode.
2. Whether the crawler can reach the site. If robots.txt blocks OAI-SearchBot, OpenAI says the site is not shown in ChatGPT search answers at all, and a CDN or bot-protection rule can block it even when robots.txt allows it.
3. How the engine reshapes the question. The rewritten query, not the buyer's wording, decides which pages compete.

In 2025, a practitioner on the SEO_for_AI forum ran one local query against a small travel blog that ranked #1 on Google and #4 on Bing. Google AI Overviews cited it first. Google AI Mode did not cite it at all, and seemed to check official sources instead. ChatGPT 5 passed it over for official park sites and bigger publications, while Bing's AI summary used only the two top-most URLs.

Same page. Same question. Four verdicts.

The mechanics explain the strangeness. In 2025, the Inspiring Tech Leaders podcast described AI Mode as running on Gemini 2.5 with a query fan-out technique: it breaks one question into sub-topics, runs several related searches at once, and synthesizes them into a single answer. The same episode placed Microsoft's AI search inside its own line, through Bing and Copilot. ChatGPT does something else again, rewriting the question before it searches at all, and one tester found the API and the chat window named almost completely different companies for the same question.

The common assumption is that one optimization serves every assistant. Google's own guidance says sites need nothing special for its generative responses, because they draw on its index plus additional fan-out queries, and that statement covers only AI Overviews and AI Mode, not ChatGPT.

Combining 5 sources points to one pattern: the retrieval stack, not the brand, decides which page an engine sees first. Ask which index each engine reads first. Then ask what that index already holds about the client.

AEO Content has run 26,577 real AI-visibility audits, and I would not let any of them fold three retrieval paths into one number. The AEO knowledge base guides separate these surfaces for the same reason. The research section below walks through what each engine did with one page.

Are free AI visibility graders, single-engine trackers and $20 audits accurate enough?

Not on their own. Free graders read markup, cheap audits hand over one PDF, and single-engine trackers watch one surface. None samples three engines repeatedly.

A practitioner on the r/seogrowth forum surveyed the free tools in 2026 and found most were paywalled, vague about methodology, or treating GEO as a checkbox list. Amsive's AI Visibility Grader was the clearest case: free, scanning for schema, FAQ structure and some basic signals, and in the poster's word "surface-level," with no breakdown by layer and no order of fixes. A commenter in the same thread went further and said any tool claiming a 60/100 extractability score "is reverse-engineering outputs that change with every model update."

I read that line twice. It is unkind and it is correct.

The other tool types fail in quieter ways. A single-site check sold for twenty dollars promises SEO, AEO, GEO, UX and compliance in one detailed PDF, a snapshot of readiness rather than a record of what any assistant said. An API that scrapes Google AI Mode returns the cited references for each question at one search credit per question, which is useful, and yet it watches one engine, and some questions return no references at all.

Then ask each vendor how its tool handles churn from one day to the next. On The GEO Show, the host reported that a previously cited domain stayed cited the next day only 37.8% of the time in Google AI Mode, 49.9% in ChatGPT and 68.5% in Perplexity. The same host's rule is blunt: never report GEO from single executions, and run a prompt "in the order of thirty to forty times in a row" before trusting what it says.

So ask the vendor how many runs sit behind each number, and whether a single scan is all you are buying. It is a moment.

A checklist tells you what the page contains. It cannot tell you what ChatGPT, Copilot or AI Mode chose to say on Tuesday, or whether they will say it again. That gap is why I prefer research-driven content built on evidence over markup alone, and why the real question shifts from which tool to buy toward what, exactly, a repeated audit across three engines should be counting.

What should a multi-surface AI visibility audit measure, and who should run it?

Score each prompt on ChatGPT, Copilot and Google AI Mode separately, many times, on a 0 to 3 scale, then benchmark against a category baseline built from 11,000+ scored domains.

The standard I hold a client audit to has five parts:

1. A tiered prompt set built from real buyer questions, commercial and informational, with pain-point phrasings such as "why X isn't working" kept apart from generic keywords.
2. Three engines, sampled separately: ChatGPT, Microsoft Copilot and Google AI Mode, each run repeatedly, never once.
3. A graded score for every answer: 0 for no reference, 1 for an indirect reference, 2 for an explicit mention, 3 for a clickable source link.
4. Mentions and cited URLs logged as different things, because a brand can be named without being linked, and linked without being named.
5. Results reported per engine, then set against the client's category, never blended into one number.

The scale is not mine. A practitioner on the r/SEO_for_AI forum published it with an audit of 100 tier-1 queries, tracking whether the brand was named, whether a URL was cited, and whether content themes were paraphrased. The first round found a direct brand reference in only 14% of prompts. After evidence-driven content and stronger internal linking, that rose to 32% within six weeks, with no ranking gain in traditional SERPs, and pain-point prompts performed 2.4× better than generic keywords. Two changes landed at once, so neither can claim the result alone.

Look at the engines that audit used. ChatGPT, Google AI Overviews, Perplexity. No Copilot. No AI Mode.

That absence is the whole argument. A buyer inside Microsoft 365 asks Copilot; a buyer on Google's results page opens AI Mode. An audit that never asks those surfaces cannot report what they say.

The benchmark matters as much as the sampling. A score of 2 means little in isolation and a great deal against peers, which is why our baseline spans 11,000+ domains scored across 15 sectors and 28 categories. A client's number only becomes a finding when it sits beside its category.

Who should run it? Someone who understands both the search layer and the plumbing beneath it. Our team includes a full-stack engineer and content infrastructure architect with 20 years of building enterprise systems, and Alex and I bring a combined 40+ years of SEO and content infrastructure experience to AEO at AEO Content. I name that not as a credential to admire. Crawlers, robots rules and sitemaps decide whether an engine can see the page at all, and the audit must be able to read them.

The AEO FAQ on audit methods covers the scoring questions clients ask most, and the next one is always the same: once the five parts are running, what does the report on the desk actually look like?

What happens to client audits that keep watching only one engine?

They keep reporting a fraction of the buyer's path. Our own record runs from a quiet site to 10 booked calls in 3 weeks, and proving that needs every surface.

I do not know which engine a client's buyer opens first. Nobody does. That is the point. Over the next two years, I expect client audits to report each retrieval stack on its own line, Bing-routed answers apart from Google's fan-out answers, because a citation on one tells you nothing certain about the other. The GEO Show described SignalForge tracking 30 prompts at 40 runs every 7 days, with a margin of error of ±2%, and that is the cadence a serious client report will be measured against.

One run is a rumor.

And yet something in me understands the pull of the single dashboard. One engine feels like certainty. It is only silence on the other two. The next audit you send a client should carry three columns, a run count under each, and a date.