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AI Platforms

Every assistant has its own definition of a good source.

Your brand does not have one AI visibility number — it has one per platform, and they usually disagree. The gaps between them are the most useful diagnostic available.

Why split them out

An average across platforms hides everything worth knowing.

The following four findings only become visible once results are reported per platform.

01
Where the gap actually isStrong on one platform and absent on another identifies which source ecosystem is missing; a single average identifies nothing.
02
Which work paid offMedia placement and review content move different platforms. Averaged together, neither effect is visible.
03
Whether the number is realOne platform moving sharply can carry an average on its own. Per-platform reporting makes this apparent.
04
Where to spend nextThe platform with the widest gap and the most reachable sources is usually the most cost-effective next move.

A note on how these pages are written

Everything on the platform pages describes tendencies observed by running the same question set across every platform we cover and recording what the answers cite. None of it is published weighting from the vendors; no vendor publishes such data.

These tendencies also change with model versions. This is why every page ends the same way: run your own question set before and after a version change, and compare. That comparison is more reliable than any conclusion you can read, including the ones stated here.

FAQ

Questions teams ask before they start

Do I need to cover every platform?

Not necessarily. Start by measuring them all — that costs nothing but an afternoon — then invest where the gap is widest and the sources are reachable. Some categories are barely discussed on some platforms, and paying to cover those is wasted.

Why do my results differ so much between platforms?

Because they weight source types differently. A brand with strong media coverage but no user reviews looks good on the platforms that lean on authoritative sources and weak on the ones that lean on social proof. The gap points straight at what is missing.

How often do these tendencies change?

The underlying preferences — factual accuracy, consistency, structure — have been stable. What changes with model versions is how strongly each is weighted and whether live search is triggered. Re-run your question set before and after a version change rather than relying on any written conclusion.

Why are ChatGPT and Gemini not covered?

This site covers the assistants used inside China. If your customers are elsewhere, both the question set and the platform list need to be determined separately, and the conclusions on these platform pages do not transfer directly.

How long does one round across every platform take?

About half a day for 15 to 30 questions at three runs each per platform. Concentrate a round into one or two days; spreading it over weeks introduces condition drift that the data cannot separate out.

Is data from before a platform update still usable?

Keep it as a historical record, but do not join it to post-update data on the same curve. Record the version change, and start a new series after it.

Find out where your brand actually stands in AI answers.

An AI visibility diagnosis across the platforms your customers use, on your real questions. No charge for the first look.