How we measure AI search

A citation is one domain cited in one AI response. That is the atom every figure on this site is built from. Counting each link separately would let a single long answer with a heavy footnote list outweigh a hundred ordinary ones, so a domain cited repeatedly within one answer still counts once — the question we are answering is which sites do models reach for, not how many footnotes they attach.

Every URL is reduced to its registrable root before it is counted, which means shares here are shares of sites, not of pages or links. Subdomains, tracking parameters and www prefixes all collapse away.

Where one AI product has more than one surface, the surfaces fold together. ChatGPT Web — the web-grounded variant — counts as ChatGPT, because it is browsing mode on the same product rather than a separate model. Splitting them would understate ChatGPT and invent a model nobody chooses to use.

A model only appears in the citation figures if its answers actually carry source links. Today that means Perplexity, ChatGPT, Claude, Gemini, Google AIO and Google AI Mode. Grok, DeepSeek and Meta AI answer without citing anything: they run in the dataset and are measured elsewhere on the platform, but they contribute no citations and can never move a share here.

One piece of small print worth knowing when you read a count ribbon: the responses figure counts sourced responses — answers that returned at least one link. It is the denominator behind "citations per answer" and nothing else.

The source is every analysis run on the BrandInsightAI platform. Clients define projects, the platform puts their questions to the models, and each answer's source list is captured exactly as it comes back. There is no panel, no scraping of the models' front ends for effect and no sampling step — the population is the set of answers the platform genuinely received.

Those answers are aggregated into weekly buckets: Monday-to-Monday ISO weeks, keyed on domain and model. The in-progress week's bucket is recomputed every hour; finished weeks are already final and are never rewritten. The living leaderboards read those buckets rather than the raw answers, which is why they can be rendered fresh on every request and still be quick.

Being honest about what this dataset is: it is made of real commercial work, so it is not a random sample of the questions the world asks an AI. It leans towards categories brands invest in measuring, and towards the markets those brands sell into. Everything here describes this dataset. Where a finding could plausibly be an artefact of that skew, we say so on the piece rather than leaving you to guess.

A week is complete once it is over. The current week is rewritten hourly as new answers land, so on a Tuesday morning it holds two days of data and by Sunday night it holds seven.

Every week-over-week comparison on this site therefore uses the two most recent complete ISO weeks. Comparing a live week against a finished one would manufacture a collapse every Monday morning and a miraculous recovery every Friday afternoon — the movement would be an artefact of the clock, not of the models.

Rolling windows behave differently and deliberately so. A four-week leaderboard does include the current week, because a share computed across the whole window is not distorted by its last few days being thin: the partial week shrinks both the numerator and the denominator together.

One project citing a domain heavily is one client's niche. Two unrelated projects citing it is the minimum evidence that the behaviour belongs to the model rather than to the brief, and that is the bar a domain has to clear before we will print its name.

Movers carry a second floor because small denominators produce spectacular percentages: a domain going from two citations to six is a 200% rise and means nothing. Requiring the citation floor in both weeks does a second job — a domain that only entered monitoring this week cannot appear as the week's biggest gainer, which is the single most common way a citation leaderboard lies to itself.

This distinction is the one worth being pedantic about. Every share is calculated against all citations in scope, including those belonging to domains too thinly evidenced to name. So the percentages on a leaderboard will not add up to 100 — the remainder is a long tail we can see perfectly well and simply will not identify. The alternative, dropping the hidden citations out of the denominator, would inflate every visible domain by exactly the amount we were trying to be careful about. A named domain's 3.1% means 3.1% of everything, not 3.1% of what survived the gate.

These pages are built out of a deliberately narrow vocabulary: cited third-party domains, AI model names, market names, closed enumerations such as content types and journey stages, period labels, and numbers. Anything outside that vocabulary is refused rather than redacted — the piece stops instead of being quietly cleaned up.

• Client brand and competitor names. No brand a client tracks is ever named, whether it is theirs or a rival's.
• Project names. Nothing identifies who commissioned the work that a figure came from.
• Prompts and queries. No verbatim question, no paraphrase, and no fan-out search string a model generated for itself. Where we report on fan-out behaviour we publish counts and structure only; the strings never leave the database.
• Owned and competitor URLs. Only third-party domains that have cleared the naming floor can appear. A URL outside that vocabulary fails the scan on sight.
• Free-text topics. Category descriptions written by a client stay private; only platform-authored enumerations are publishable.

Two independent gates enforce this, and both fail closed. Before any figures reach a language model, they are walked leaf by leaf against the allowed vocabulary — map keys included, because an object keyed by brand name is the classic way a dataset leaks — and anything unrecognised aborts the article. After the prose is written, it is scanned again against a live list of every brand name held on the platform, and every URL it contains is checked against the cited-domain vocabulary. A piece that trips either gate does not get edited into shape; it fails.

That brand list is assembled at scan time and thrown away. It is never stored on an article, never written to a log and never leaves the server.

Every number is computed in SQL or code before a language model is involved, from the same snapshots the living leaderboards render. The figures, the charts and the data tables in an article all come out of that one computation.

The model's job is narrow by design: it writes about the numbers it is handed. It has no database access, no lookup tool, and an instruction to use only the supplied figures. If a sentence would need a number that was not provided, the sentence does not get written — and where synthesis fails or trips a gate, we fall back to a deterministic write-up of the very same numbers.

Re-running the prose never re-runs the arithmetic. An editor can send a draft back to be rewritten, and it is rewritten over the stored facts: the figures in the second version are identical to the first, because they were never recalculated.

Nothing publishes itself. The generator can only produce a draft; a person reads every article and explicitly approves it before it becomes public. Each piece then carries its sample size in a ribbon at the top and a method-and-limitations block at the foot.

Finally, the framing. This is observational data. We can see which sites models cite and how that changes; we do not run controlled experiments, so nothing here establishes cause. Findings are written correlationally on purpose, and where a plausible confound exists we name it rather than letting a clean sentence imply more than the data carries.

Every chart on this site is a figure with three parts, and it is never published with fewer.

The drawing is inline SVG with an accessible role — no scripts, no external files, nothing to load and nothing to block. The caption states the claim in words rather than restating the title, so a reader who takes only the caption still takes away something true. The table holds the underlying numbers as real HTML, sitting in a collapsed block beneath the chart and present in the page source whether it is opened or not.

The point of the third part is that a sighted reader, a screen-reader user and a crawler all get the same numbers. If you want to check a figure, quote it or reuse it, expand the table — that is what it is there for.

A citation is not a visit. We measure what models cite, not what people click. A site can be cited constantly and receive very little traffic from it, and the relationship between the two is not something this dataset can settle.

Models change without announcing it. A step change in a chart can be a retrieval or ranking change inside a model rather than anything a publisher did. We flag abrupt discontinuities where we spot them, but we cannot always tell the two apart from the outside.

Coverage follows the dataset. Categories and markets that clients invest in measuring are represented far better than those they do not. Absence from a leaderboard is not evidence that a site is never cited — it may simply sit outside what we observe.

Shares are relative. A domain can lose share in a week when its own citation count grew, because something else grew faster. Where that has happened we say so instead of writing the fall as a decline.

If something here is wrong, we would rather fix it than defend it. Corrections are made in place: the page keeps its original URL rather than being quietly replaced or taken down, and a dated correction note is added to the page saying what was wrong and what changed. Nothing is removed from the record.

Where the error is in the computation rather than the wording, the affected figures are recomputed and every page that carried them is updated — including any chart's data table, which must always match the drawing above it.

Living leaderboards are a special case. They re-render from the current snapshots on every request, so a fix to the underlying data propagates on its own within the hour and needs no correction note. When a fix changes a definition — a floor, a window, a fold — we note it on this page instead, because a definition applies to every page at once. This page's last review date is at the top for exactly that reason.

If you believe a figure on this site is wrong, tell us through the contact route on our FAQ page, ideally with the page URL and the number you are querying.