Type one search phrase. See what five engines answer.

Most visibility tooling is built for scale: hundreds of keywords, a score, a trend line. That is the right shape for monitoring, and the wrong shape for the question you usually have first - what does an AI engine actually say when someone asks about this?

Analysis is the ad-hoc probe. There is no template to set up and nothing to schedule. You type the phrase, you get five answers back, and you read them. It is deliberately the smallest useful unit of the job.

It matters because the answer and the ranking come apart more often than people expect. A phrase where you sit comfortably in Google's top three can produce a ChatGPT answer that names three competitors and never mentions you. ChatGPT, Claude and DeepSeek re-rank the same Google result set we fetch, so when they leave you out they are leaving you out of a list you were already on - which is the sharper version of the problem, not the softer one. Perplexity and Gemini retrieve for themselves, so they can disagree about the sources as well as the summary. Classic ranking signals and citation signals are not the same signals, and the only reliable way to find out which of your phrases behave that way is to ask.

The other reason is language. The generated answer is the product now. Knowing you were not cited is thin; reading the paragraph that got shown instead, and seeing which pages it was built from, tells you what the engine believes about your category and who it currently trusts to explain it. That is a page you can go and read, and often a gap you can go and fill.

When a phrase turns out to matter, move it into a saved template in the Visibility Scanner, which runs the same checks across seven answer surfaces on a repeatable basis and lines the runs up so you can see movement.

The position is a fact about one answer, from one engine, at one moment. It is not a rank in the way a Google position is a rank. Answer engines are not deterministic: the same query can return a slightly different answer and a slightly different citation list an hour later, because the model samples and because its retrieval layer sees a moving index underneath it.

So read a single run as evidence, not as a score. "Not found" on one engine on one day is a prompt to look; "not found" over two weeks, on surfaces that retrieve independently of each other, is a finding. Four absences that all trace back to one Google result set are one absence counted four times, which is why every engine label here names the corpus its answer was built from. This is exactly why the tracked version exists - one measurement cannot separate a real absence from the noise, and a series can.

The citation list is the more durable signal in any single run. Which domains an engine reaches for is far more stable than the exact wording of its answer, and it tells you what kind of source it trusts for your category: your competitors' own sites, a review aggregator, a marketplace, a news outlet, a forum thread. That pattern usually points straight at the work.