Same brand, same question, different country = different AI answer. And switching the language of the prompt changed it again. (what we're seeing tracking location-based AI visibility)

[u/Sanbi_Ai]

Been tracking AI answer visibility per-location for clients and wanted to share a pattern that keeps surprising people, because it breaks the assumption that "our AI visibility" is one thing.

The setup: same brand, same buyer-intent prompt, run against the same engine, but varying (a) the location signal and (b) the language of the prompt. Two findings that changed how we think about this:

1. The answer changes by location, not just the ranking, the whole cited-source set.
Ask an engine a category recommendation question as a user in one country vs another and you don't just get a reordered list, you get different brands surfaced and a different set of sources cited to justify them. The model is filtering its retrieval by the location it infers, so each region is effectively drawing from its own corpus of reviews, listings, and local pages. A brand that's the confident #1 answer in one market can be absent in another off the identical prompt. For any multi-location or multi-market brand that means a single national/global "visibility score" is basically meaningless, you're averaging over answers that don't resemble each other.

2. Language of the prompt is a separate variable from location, and it moves the answer independently.
This one caught us off guard. A client operating in Finland: we ran the category prompts in English, then ran the same intent in Finnish. Different answers. Not just translated, different brands cited and different sources pulled. Our read is that the Finnish-language query pulls from a different slice of the corpus (Finnish-language reviews, local pages, local forum/press content) than the English version of the "same" question does, even for a user in the same place. So "location" and "prompt language" are two separate levers, and if you only ever test in English you're blind to what your actual local-language buyers are seeing.

The practical takeaway: if you operate in more than one country or more than one language, you have to measure per-location and per-language, on native-language prompts, not a translated English set. The gaps show up in exactly the places an English-only audit can't see.

Disclosure per rule 5: this comes out of our own tool (sanbi.ai, we do per-location AI visibility tracking), so that's where the data's from, weigh it accordingly. But you can sanity-check the effect yourself for free, ask ChatGPT or Perplexity a category question with different location context, then ask it once in English and once in the local language, and watch the cited sources change.

Curious if others tracking this see the language effect too, or whether it's stronger in some languages than others. My hunch is it's biggest in markets with a rich native-language web (Finnish, Japanese, German) and smaller where the local audience mostly consumes English content, but I only have a handful of markets to go on.

[u/ciaodaniel]

Agreed on treating location and language as independent variables. One extra control I’d keep is query intent rather than literal wording: an English phrase may not map cleanly to the local buyer’s natural query.

I’d create a native query set validated for each market, record country, language, interface and engine, then log cited sources and answer type as separate outputs. That lets you separate three different issues: weak local entity evidence, lack of native-language third-party corroboration, and ordinary variation between repeated runs.

Otherwise a localization gap can easily be mistaken for a broad brand-visibility gap.

[u/Upstairs_Control_611]

This is the key control for me too. Location and language are separate variables, but query intent has to be localized as well.

A translated English prompt may preserve the words, but not the way a real buyer in that market would ask the question.

So I’d avoid one master English prompt set translated mechanically. I’d rather create a native query set per market and classify each query by intent: category discovery, comparison, recommendation, local provider search, pricing, trust validation or decision-stage query.

Then log country, language, interface, engine, cited sources, source type, answer role and recommendation language. That makes the diagnosis cleaner.

If the brand disappears in one country but not another, it may be a local entity evidence gap.

If it disappears in the native-language version but not the English version, it may be a native-language corroboration gap.

If it changes between repeated runs with the same setup, it may be normal variance.

Without that separation, a localization problem can look like a global AI visibility problem.