Why take the definitions from us
Definitions are cheap to assert and expensive to keep. The wording here is the wording in the method we publish, in the registry where every public figure carries its source and its date, and in every report we put out. Nothing gets a looser meaning on the way from a measurement into an ad.
That is what makes a definition here checkable rather than merely stated: quote a sentence, then follow it to the measurement method and to the entry in the public claims registry that holds the matching figure, its source and its date.
What is AI search visibility?
AI search visibility is the degree to which a brand or its content is named or cited inside the answers generated by AI search systems, rather than in the ranked links beside them.
CiteAngle measures it by putting a fixed query set to the same answer surfaces, repeatedly, and recording for every cell whether the brand appears in the answer body and whether a page from its domain is offered as a source. Rates come back with an interval attached, and the conditions behind them are published in the methodology.
How do SEO, AEO, GEO and AIO differ?
They name four different targets on the same screen: ranked results, direct answers, generated answers, and the AI layer a search engine places above its own results.
The work overlaps and the measurement does not. A page can rank without ever being cited, and it can be cited by an assistant that never sends a visitor. LLMO belongs in the same table because assistants increasingly answer without a search page in front of them.
| Term | What it targets | What gets counted | Where the work lands |
|---|---|---|---|
| SEO Search engine optimization | TargetsRanked organic results | CountedIndexation, position, organic entries | Where the work landsTechnical health, content, entity consistency, internal links |
| AEO Answer engine optimization | TargetsSurfaces that resolve a question directly | CountedWhether the answer is adopted and which sources travel with it | Where the work landsAnswers that finish the question, evidence, comparisons, entity consistency |
| GEO Generative engine optimization | TargetsGenerated answers | CountedDirect citations and brand mentions inside the generated text | Where the work landsOriginal data, first-hand expertise, source material worth quoting |
| AIO AI-driven search as a category | TargetsThe AI layer above search results | CountedAppearance, mention, citation and the traffic that follows | Where the work landsSearch fundamentals plus evidence a layer can attribute |
| LLMO LLM optimization | TargetsAssistants answering without a search page | CountedWhether the facts are retrieved, attributed and restated correctly | Where the work landsMachine-readable facts, consistent naming, sources an assistant can resolve |
What is AEO?
Answer engine optimization is the practice of shaping content so that a surface which answers a question directly adopts that answer and links the page it came from.
Rank is not the unit here. The question is which answer gets used, and which sources are carried along when it is.
What is GEO?
Generative engine optimization is the practice of building source material that a generative answer can quote, so the brand is cited and named inside the generated text itself.
Our reports keep those two events apart. A citation and a mention are counted separately, on their own denominators, because they move for different reasons.
What does AIO refer to?
AIO covers visibility and traffic across AI-driven search as a whole, including the AI layer a search engine places above its own results, and it is kept on a denominator separate from that layer's own metrics.
The abbreviation gets used for two things — AI Optimization and AI Overview — so we keep the reporting fields apart. A word should never quietly change what a number is divided by.
What is LLMO?
LLM optimization is the practice of making a brand's facts easy for a language model to retrieve, attribute and restate correctly, whether or not a search page sits in front of the answer.
GEO and LLMO overlap in the work and differ in the measured event. GEO asks whether the generated answer cited or named you; LLMO asks whether what it said about you was accurate and traceable to something you own.
How do mention and citation differ?
A mention is the brand name appearing in the body of an answer; a citation is the answer presenting a URL from that brand's domain as a source.
Citations resolve deterministically against the domain. Mentions need entity resolution, because a name can belong to more than one company, and counting a namesake as you is how a report ends up flattering. Blend them into one score and neither event can tell you what moved.
One answer screen, two separate events
Answer body → mention
… for this kind of work, brand name is usually among the options people compare …
The name appeared in the text. Counted through entity resolution, so a namesake elsewhere is not counted as you.
Source list → citation
Sources: example.com/page
The answer offered a URL from the domain. Counted by deterministic URL matching against the domain under measurement.
What is an unobserved AI answer?
An unobserved AI answer is a planned measurement cell where the request and the capture both worked, but the surface produced no AI answer for that query at that moment.
There is nothing in such a cell to judge, so it stays out of the citation and mention denominators and is published as its own count instead. Full-grid accounting means every planned cell is accounted for in one of three states rather than only the ones that came back clean. That is what stops a thin run from reading like a strong result.
Where a planned cell can end up
State one
An answer came back and was judged for mention and citation.
Counts toward the rate denominators.
State two
The surface produced no AI answer for that query at that moment.
Kept out of the rate denominators, published as its own count.
State three
The capture itself failed and nothing usable was recorded.
Reported as a failed measurement, never folded into the other two.
What is answer presence rate?
Answer presence rate is the share of queries for which an AI answer surface appeared at all, and it moves with platform policy rather than with anything a brand does.
No answer means no denominator for citation or mention, so presence belongs next to the numbers as a stated condition. We print it as context and read it as a signal about the surface, not as a scoreboard for the brand.
What is answer variance?
Answer variance is how far the citation and mention results move when the same query is run again under the same conditions.
One reading cannot tell you whether a difference is real movement or the spread you would have seen anyway. Repeats exist for exactly that reason, and a report shows the spread instead of a single flattering number.
What is a confidence interval?
A confidence interval states the range of values consistent with the observed sample, given how many usable observations there were.
Printed beside a citation rate, it makes the difference between a stable finding and a small-sample artifact visible on the page. When two intervals overlap, we write that down and say what would settle it, which is usually another round on the same grid.
What is a verifiable measurement?
A measurement is verifiable when its query set, surfaces, repeat count and measurement dates are published, so the same conditions can be run again and the result checked by someone else.
A figure built on conditions nobody can see is a figure nobody can check, and that is a thin thing to sign a contract on. Ours are listed in the public claims registry, each with the kind of source behind it and the date it was established.
What is a sealed hash ledger?
A sealed hash ledger records a fingerprint of the measurement setup and of its results at the moment of measurement, so later edits and backdating become detectable.
Our own canonical runs are sealed this way. The identifier of the run, the configuration fingerprint and the query-set version are all fixed at the moment of measurement, not when the report gets written, so a single changed character shows up.
What is a t0 baseline?
A t0 baseline is the starting measurement, sealed before any change is made, and it is the reference every later comparison is judged against.
Once a change goes live, the state before it cannot be recreated. A baseline reassembled afterwards is a weaker object than one sealed on the day, and sealing is cheap while the measurement is still running.
What belongs in a measurement receipt?
A measurement receipt is the record that ties a published figure back to the queries, surfaces, repeat count and dates behind it.
It is what lets a reader walk a number backwards to the run that produced it. Our published measurements carry those fields in the body of the text, where a buyer can act on them, rather than in a footnote written to be skipped.
Same wording, same denominators, everywhere we publish. Read how the grid is built and why the repeats are there in the measurement methodology, or open the claims registry and check any figure we publish against the source and date registered with it.