A marketing lead searched for almost exactly this recently: they needed to see which pages Google AI Overviews and Claude were pulling from when those tools mentioned their brand, and they wanted to know who does this. It is a good question, and a precise one. They had already accepted that AI was talking about them. What they could not see was the machinery behind it.
This piece answers that question in plain terms. What it takes to see the pages behind an AI mention, how far you can get on your own, and where a done-for-you service earns its keep.
Why teams reach this question
You get here once the first surprise wears off. You have run your own brand through ChatGPT, or a colleague forwarded a screenshot of an AI Overview that named you, and the novelty gives way to a harder question. Where is that coming from? Which of our pages did the model read? And when it recommends a competitor instead, whose pages is it reading then?
This matters because you cannot fix what you cannot see. If Claude recommends a rival and leans on a Reddit thread and a review site, that points you at the sources to work on. If Google AI Overviews quotes your pricing page but never your comparison page, that tells you which page is doing the job and which one is invisible. Without the page-level view you are guessing, and guessing is expensive when the fix is content and the timeline is months. It is the same gap that leaves so many brands missing from AI answers without knowing why.
What the answer actually requires
Two steps sit behind a proper answer.
First, grounded runs. You query each engine the way a customer would, then capture the sources the engine used to build its reply. The engines that read the live web attach citations to what they say. You log every one, per engine, per query, so nothing is lost between the answer on screen and the record you keep.
Second, mapping. You take each citation and resolve it to a specific URL, then sort those URLs into your own pages and third-party pages. A mention backed by your own guide behaves very differently from one backed by a directory, a publisher or a forum, so the split is where the meaning lives. This is what people mean by page-level citation mapping, and it is closely tied to how these engines choose their sources in the first place.
One caveat runs through both steps. A single query run once is a snapshot, not data. AI answers shift by session, location and phrasing, so one check tells you little on its own. The signal comes from running the query enough times to see which pages appear consistently, rather than which page happened to surface on the morning you looked.
The DIY version, honestly described
You can do a version of this by hand, and for a first look you should. Each engine surfaces its sources a little differently:
- Perplexity is the easiest. It shows numbered sources on every answer, so you can read straight off which pages it used.
- Google AI Overviews and Google AI Mode link out to the pages behind the summary, usually to the side or beneath it.
- ChatGPT and Claude list their sources when they search the web for an answer. When they reply from memory without browsing, there is no citation to inspect, which is itself worth knowing.
By hand, for one question in one session, you can see roughly which pages an engine reached for. That is genuinely useful, and it costs you nothing but time.
What you cannot see by hand is the shape of it. Whether those same pages show up next time. How often your page wins the mention versus a third party's across fifty different phrasings of the same question. How the picture differs in the United States versus the United Kingdom, where the answers are often not the same. Whether the pattern is drifting month to month as models retrain and competitors publish. A single reading cannot tell you any of that, because a single reading is not a trend.
It also does not scale. Checking one query across six engines by hand is twenty careful minutes. Doing it for a hundred queries, repeated weekly, across two markets, is a standing job rather than a spot check. That is the point where hand-checking quietly stops, and where most teams go looking for someone who does this properly.
The done-for-you version
This is the work we run as a service, and it is built on the technology we developed to do exactly this at volume. We query the six engines we track, ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews and Google AI Mode, with the questions your buyers actually ask, and we repeat each query enough times to stabilise the variance rather than trust a single answer.
For every reply that names your brand or a competitor, we log what the engine cited and map it back to the page. You end up with a clear picture of which of your pages earn the mention, which third-party pages the engines lean on, whether that is a review site, a publisher article or a community forum, and which competitor is winning the citation you want. Reverse-engineering those competitor citations is often where the sharpest actions come from, and it is the backbone of a working AI citation strategy.
Because we store every check, you see the trend and not just today's reading. What some teams call share of voice we report as Share of Memory: how often you appear at all, how often you are actively recommended rather than merely listed, and how strong the sources behind that recommendation are. Read together, those tell you whether a mention is solid or fragile.
A fair word on where tools fit, because plenty of good ones exist. Monitoring dashboards such as Profound, Peec and Otterly track mentions across engines and show you where you stand. Dashboards show you where you stand. A service reads the citation pattern and tells you which page to fix, which source to pursue, and which competitor to take apart. Our angle is that expert-run analysis sitting on top of the data, so you get an answer to act on rather than another login to manage.
Where to start
The cheapest way to find out whether any of this applies to you is a free AI visibility check across ChatGPT, Claude, Gemini, Perplexity and Google AI. It shows whether AI mentions you at all and who it recommends instead, which is the honest first question before you spend anything on mapping the pages behind those mentions.
If the check shows a gap worth closing, a short call is the next step. We will walk you through what the engines are citing about your category and what a page-level mapping engagement would surface for your brand specifically.
Frequently asked questions
How can I see which pages Google AI Overviews and Claude pull from when they mention my brand?
Run the query through each engine and capture the sources it cites, then map every citation back to a specific URL and sort those URLs into your own pages and third-party pages. Perplexity, Google AI Overviews and Google AI Mode show their sources directly, and ChatGPT and Claude list them when they search the web. For a handful of queries you can read this off by hand. Across a hundred queries, repeated over time and across markets, it needs a system that logs every citation and keeps the history. That page-level citation mapping is the service we run.
Can you continuously monitor my brand's share of voice across ChatGPT, Gemini and Perplexity?
Yes. On a retainer we run your core queries across the six engines we track, including ChatGPT, Gemini and Perplexity, and we store every check so you see the trend rather than a single reading. Core queries run daily for retainer clients. What some teams call share of voice we report as Share of Memory, alongside how often you are actively recommended and how strong the sources behind the recommendation are.
How do brands monitor mentions across AI platforms such as ChatGPT, Perplexity and Google AI Overviews?
The two routes are a monitoring dashboard you run yourself or an expert-run service that runs it for you. Dashboards such as Profound, Peec and Otterly track mentions across engines and show you where you stand. SearchIntel sits on the other side of that line: we run the queries across six engines, log what each answer cites, map it back to the page, and tell you what to change. Start with a free check to see whether you have a problem worth tracking.
What is page-level citation mapping?
Page-level citation mapping is the practice of taking every source an AI engine cites when it answers a query and resolving it to a specific URL, then sorting those URLs into your own pages and third parties'. It shows which of your pages earn a mention, which review sites, publishers or forums the engines lean on, and which competitor is winning the citation you want. It turns a vague sense that AI mentions you into a page-by-page map you can act on.