How We Track Whether AI Cites Us

Every week there is a new tool that promises to track your “AI visibility,” and most of them are selling a number they invented to a market that can’t check it. We track whether AI cites us for a living — a small fleet of sites, measured monthly — and our whole stack costs nothing. What it costs instead is discipline: knowing what each instrument actually measures, which cells in which reports lie, and which three numbers are worth your attention. This is the stack, the method, and the traps, from the reports we pulled today.

Start with the engine’s own ledger

Bing Webmaster Tools has an AI Performance section, it is free, and almost nobody in this business has opened it. It reports two things no third-party tool can know firsthand: the grounding queries — the actual questions for which Copilot and Bing’s AI answers pulled your pages as sources — and your citation share of each one. A third-party tracker samples AI answers from the outside and guesses. This is the engine’s own bookkeeping. If you do nothing else in this article, connect your site there and pull the two CSVs: AI Search Queries and AI Page Stats.

Read the two reports against each other, because individually they will lie to you in specific, learnable ways. Today, a filtered export for one of our sites showed a grounding query with 2,796 citations and a citation share of 0.00% — an impossible pair. The page report for the same window showed our page taking 2,790 of those citations. The share cell in a filtered view was simply broken. The lesson is not “the data is junk.” The lesson is that a citation number is only as good as the second report that confirms it.

The three numbers worth watching

One: citation share on the queries that pay. Not total citations — share, on the handful of questions your buyers actually ask. Share is a rate over a trailing window, not a switch that flips, so judge it the way you’d judge a batting average: this month’s window against last month’s, same instrument, same site. A share that climbs two windows in a row is the engine changing its mind about you. A share that jumps once is weather.

Two: the per-page trend. Page-level citations tell you where the engine is consolidating. Our certification page for restoration contractors climbed four straight exports — 1,635, then 1,913, then 2,454, then 2,790 citations — while its near-duplicate twin collapsed from 304 to 6. That is the engine picking a winner between two similar pages, in public, in numbers you can watch. When you see it, feed the winner: better structure, tighter answers, the cluster’s links pointed at it. When you see the split instead — two pages dividing one topic’s citations — you have found the leak.

Three: new grounding queries. Every export contains questions you have never seen, some of which you are already being cited on without knowing it. Those are the cheapest wins in the whole system: the engine has already decided you are relevant; a page that answers the question directly, in the asker’s words, converts a toehold into a position. Half of what we publish starts as a line in this report.

The second instrument: what people do after the click

Microsoft Clarity is the other free half of the stack — session recordings, heatmaps, and an export API — and it answers the question citation reports can’t: what happened when the visitor arrived. Its limits are just as instructive. Clarity masks typed text by design, so a search box can light up with clicks while the actual search terms never appear in a recording. We learned to read the terms out of the URLs instead — a completed site search carries its query in the address — and when we pulled three days of URLs across two sites this week, we found a search box that had drawn 69 clicks and zero completed searches. The box was being touched constantly and used never. No dashboard said that. Two exports and ten minutes of reading did.

Clarity also ships AI summaries of your traffic, and our rule for those is blunt: a summary is a lead, never a fact. Session counts and page claims get settled by the raw export or a live fetch, because the one time it matters is the one time the summary will be confidently wrong. Treat every AI-written summary of your own analytics the way you’d treat an AI-written summary of anything: as a rumor with good formatting.

What the paid trackers add — and what none of them can see

Paid visibility trackers earn their money on breadth: they run panels of prompts against ChatGPT, Claude, Perplexity, and Google’s AI surfaces, where Bing’s report covers only its own family. If your buyers live on those engines, a panel tool tells you things the ledger can’t. Buy breadth if you need it. But understand what you’re holding: a sample of answers, taken at a moment, from accounts and locations that may not match your buyers. It is a poll, not a census. The engine’s own report is the census for its engine — narrower, and exact.

What no tool in this category can see is your side of the wall: which pages your own visitors touch, what they search for on your site and fail to find, which of your URLs the bots are actually crawling. Roughly six in ten visits across our fleet this month were automated, and that share climbs every month. Any visibility number that doesn’t ask “of the humans or of the machines?” is averaging two different worlds into a fiction. Your own logs and exports are the only place that question gets an honest answer.

The tool categories, compared honestly

Search for answer engine optimization tracking tools and you will find four kinds of instrument wearing the same label. Engine-native ledgers — Bing Webmaster’s AI Performance reports are the working example — show the grounding queries and citation shares from inside one engine family: narrow, exact, free. Behavior analytics like Microsoft Clarity show what visitors did after the click: also free, also first-party, blind to anything that happens off your site. Paid panel trackers run batteries of prompts against ChatGPT, Claude, Perplexity, and Google’s AI surfaces and report how often you appear: broad, sampled, and only as representative as their prompt list and accounts. Your own server logs and exports are the unglamorous fourth category, and the only one that separates human visits from the bot traffic that now makes up the majority of most sites’ requests.

None of the four replaces another, and the marketing of each pretends otherwise. The ledger can’t see ChatGPT. The panel can’t see your visitors. The analytics can’t see the citation that never became a click. A working stack layers them in that order — ledger first because it’s ground truth for its engine, analytics second because behavior is where money is made, panels third if your buyers live on engines the ledger doesn’t cover, and your own logs underneath everything as the lie detector. Anyone selling you a single tool as the whole answer is selling the tool, not the answer.

The discipline is the product

The stack is two free Microsoft tools and a spreadsheet habit. The method is: same reports, same window, read against each other, three numbers, every month, and a standing suspicion of any figure that arrives without a second source. None of that is purchasable, which is exactly why the tools market would rather sell you a score.

Skip the score. Pull your grounding queries. Find the page the engine is consolidating onto and the twin it is starving. Read your new questions like a suggestion box written by the market itself. That is tracking AI visibility — not watching a gauge, but reading the engine’s mail. It has been sitting in a free dashboard the whole time.