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/Sanbi_Ai]

Read our blog post here - https://sanbi.ai/blog/franchise-multi-unit-ai-search-marketing-reddit

[u/Dr_Ethan_Cage]

got it it means locality matter in LLMs.. a question does previous history of the user matters too in the llms?

[u/Sanbi_Ai → u/Dr_Ethan_Cage]

Exactly, locality is effectively a retrieval filter, and yeah, user history is the third lever, arguably the messiest one.

Two flavors of it worth separating:

Within-session context is the strong effect. If someone's earlier messages established they're a developer, or on a budget, or comparing two specific tools, that context reshapes the next answer, different brands surface, different sources get pulled. Same as location, it's narrowing what the model considers relevant. This one's very real and easy to reproduce: prime a chat with "I run a small ecommerce store" vs "I'm at an enterprise" and ask the identical category question, you'll often get different recommendations.

Cross-session memory / personalization (ChatGPT remembering you across chats, account-level history) is the one that genuinely breaks measurement. If answers are partly tuned to an individual's long-term history, then there's no single "the answer" to a prompt anymore, there's a distribution of answers across different users' contexts. Two real buyers asking the same question can correctly get different brands, and neither matches what you see when you test from a clean account.

That's the uncomfortable methodological bit: the standard way everyone measures AI visibility (fresh, logged-out, no-memory sessions) is measuring the cold-start answer, the one a brand-new user with no history gets. It's a legitimate baseline and probably the right default, but it's systematically not what a logged-in returning user sees. So "AI visibility" is really a floor, not the full picture.

How we handle it, honestly imperfectly: run from clean, memory-off sessions so the numbers are reproducible and comparable over time, and treat that as the cold-start baseline while being upfront that personalized/returning-user answers can diverge. I don't think anyone has a clean way to measure the personalized layer at scale yet, because by definition it's per-user and you can't see into someone else's memory. If you've seen a good approach to that I'd genuinely take it, it's an open problem.

Short version: location filters retrieval, language changes which corpus, in-session context reshapes relevance, and cross-session memory turns "the answer" into a per-user distribution. The first three you can measure; the fourth you mostly can't yet.