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BUYING QUESTION

What platform coverage really means in an AI visibility tool

"Supports six platforms" is the easiest line to compare and the easiest to say loosely. Here is what to ask before you treat coverage as a buying criterion.

chuan 9 min read

In short

  • Platform coverage is three different claims at once: we can query it, we store the full answer, and we can place results from different platforms on one axis. Most vendor pages only promise the first.
  • Cross-platform scores are the hardest part to do honestly, because the same question does not return a comparable citation set on different assistants.
  • Ask what is stored, not what is listed. A platform name in a table is not the same as an answer you can re-read next month.
  • One platform measured deeply and six measured shallowly answer different questions. Neither is automatically better; pick the one that matches the decision you have.

The same sentence, three different products

"We support ChatGPT, Perplexity, Gemini, Claude, Copilot and Google AI." That sentence appears on most vendor pages in this category, and the same words can describe three quite different products.

It can mean the tool can send a question to each of those services and show you an answer. It can mean it stores the full answer text and the cited pages, so you can read them again in a month. Or it can mean it compares your performance across those platforms, which is a much larger claim than either of the first two.1

  • Query: a question was sent, and an answer came back.
  • Store: the answer text, the model, the date and the cited URLs were kept.
  • Compare: results from different platforms were placed on a common footing, and the vendor explains what they did about the fact that platforms do not answer alike.
  • Only the third claim requires that explanation. The first two do not.

Why cross-platform numbers do not line up

A study sampling citation behaviour across Perplexity, OpenAI's search product and Google Gemini found citation distributions following a power-law form and varying substantially across repeated samples. The same measurement question, asked of different platforms, does not produce a set of numbers you can lay along one axis.1

That does not make cross-platform tracking useless. It makes an unexplained cross-platform score useless, which is a narrower and more specific problem, and a fair question to ask about any combined figure you are shown.

Depth and breadth answer different questions

A tool covering six platforms usually runs a small number of questions on each one. A tool covering one platform can afford to run more of them, store more of what came back, and repeat the same set on a schedule. Both are reasonable products. They are just answering different things.

  • Breadth answers: how does the shape of my visibility differ between assistants?
  • Depth answers: is my number for this question set stable, and what changed between runs?
  • Breadth spends its budget across platforms. Depth spends it on repetition.
  • Knowing which of those two questions is yours is most of the decision.

Six questions to ask before you compare coverage

If a vendor page lists platforms without saying which of the three claims above it is making, one email with these questions will get you a usable answer.

  • Which platforms are in production today, and which are planned? Ask for the two lists to be separated in writing.
  • For each platform, does the tool store the full answer, or only a score?
  • Are results recorded per platform, or merged into one figure?
  • How often is each platform checked, and can you re-run a fixed question set on demand?
  • If there is a combined cross-platform number, how was it normalised?
  • When a platform changes its model, what happens to your earlier runs?

What we do, stated plainly

AI Cite Who checks ChatGPT only. Perplexity, Gemini, Claude, Copilot and Google AI are not supported today, and no date has been announced for any of them.

The check runs through the search-capable path ChatGPT itself uses, so what gets recorded is the answer, the model that produced it, and the public pages returned alongside it.3 Whether those pages can be read at all is governed by published crawler rules, and they are worth reading before blaming a tool for a missing citation.2

The narrower coverage is also part of why the product can keep the full answer text, keep the cited pages, and repeat an identical question set on a schedule on the Pro plan, instead of spending the same effort across six moving targets.

If your decision depends on comparing assistants with each other, this is not the tool for it. The checklist above will tell you that in about five minutes, and it is cheaper to find out now than after a billing cycle.

What a specific coverage claim looks like

Coverage claims are usually vague because vague is safer. A specific one is easy to recognise, and it is worth asking for even after you have decided to buy.

Here is the shape of an answer that can actually be checked. It names the platform, says what is stored, states the cadence, and states what is not covered, all in the same paragraph rather than split between a feature table and a footnote.

  • Named: "ChatGPT, through the search-backed path, checked daily."
  • Stored: "The full answer text, the model version, the run timestamp, and every cited URL."
  • Cadence: "The same question set can be re-run on demand; scheduled repeats are on the top plan."
  • Not covered: "Perplexity, Gemini and Copilot are not supported, and no release date is announced."
  • Uncertain: "A single run does not separate two brands whose numbers differ by a few points."

How to decide for yourself

Write the decision down first. If it is which assistant should we prioritise, you need breadth, and you should accept up front that each platform will be measured shallowly. If it is whether this specific claim about our category is stable, and whether it moved after we changed the page, you need depth, and one platform is enough to answer it.

The trap is buying breadth to answer a depth question. It is an easy trap to fall into, because breadth is far simpler to put in a table.