Does AI search recommend different vendors in different countries?
Yes, and the difference is larger than most teams assume. FTA Global ran 1,200 buying questions across 10 categories, 12 cities and 7 languages on ChatGPT and Gemini for its Context Report, and reported in September 2026 that nearly 3 of the top 5 recommended brands changed once the buyer moved away from New York and English, with 159 of the 206 brands it saw appearing in only one country. The mechanism is retrieval rather than translation: engines infer location and pull different sources for it. If you sell in more than one market, your question set needs to be run from each of them, and the pages worth localising are the ones where the answer genuinely differs by country.
Does AI search recommend different vendors in different countries?
AI search does recommend different vendors in different countries, and in business categories the overlap between markets can be small. A shortlist produced for a buyer in New York and one produced for the same question in Mumbai or Berlin frequently share only a name or two.
The clearest published test of this is FTA Global's Context Report. Its testing, released in September 2026, ran 1,200 buying questions across 10 business categories from 12 cities in 7 languages on ChatGPT and Gemini, repeating each question five times, and found nearly 3 of the top 5 recommended brands changed when the buyer moved away from New York and English.
Category matters more than country. The same report found money-related categories moving most, with about 4.4 of the top 5 brands changing across markets, against roughly 1.5 for everyday categories such as video conferencing and cloud storage. Its most extreme case was HR software, where none of the top United States brands appeared in any other country tested.
For a B2B company the practical consequence is unpleasant and useful. Being named in your home market tells you very little about whether you are named in the markets your sales team is trying to open, and a single global visibility number averages those together into something nobody can act on. Our guide to tracking AI visibility across engines covers how to split a series so the markets stay separate.
How much do the recommendations actually change between markets?
The change between markets is big enough that market-level measurement is not a refinement, it is the measurement. The figures below come from the FTA Global testing, which is the most specific published breakdown at the time of writing.
| What was measured | Finding | What it means for you |
|---|---|---|
| Top five overlap across markets | Nearly 3 of the top 5 brands changed away from New York and English | A home-market shortlist is not evidence about any other market |
| Brands seen in one country only | 159 of 206 brands surfaced in a single country | Most vendors in these answers are effectively local, including yours |
| Money categories | About 4.4 of the top 5 brands changed, against roughly 1.5 for everyday categories | Regulated and payments-adjacent products need per-market work first |
| Agreement between engines | ChatGPT and Gemini shared about 3.2 of the top five brands for the same question in the same city | Engine and market are two separate axes, so measure both |
| Run-to-run consistency | The same question produced a consistent top brand in only 35% of tests | One run per market is not a measurement, it is an anecdote |
The payment gateway example in the report makes the pattern concrete. Asked from New York, the assistants surfaced Stripe, Square and PayPal; asked the same question from Mumbai, they surfaced Razorpay, Cashfree and PayU, with no names in common between the two city-level results.
Treat the exact percentages with the care any vendor-published study deserves. FTA Global is a marketing firm reporting its own methodology, and the figures have not been independently replicated. The direction of the finding, though, matches every other published test of location effects we could find this year, and it matches what happens when you run your own prompts from two markets.
Why do AI engines answer differently by location and language?
AI engines answer differently by location because location changes what they retrieve, not because a translation layer sits on top of one global answer. The assistant infers where you are, searches accordingly, and writes its answer from whatever that search returned.
Location is inferred rather than declared. Evertune's analysis of how AI results change by language and location describes ChatGPT using IP address data to work out nearby places for queries where proximity matters, which means a buyer's network, not their stated market, shapes the retrieval set.
The corpus that gets searched is still heavily global, which cuts both ways. xFunnel's analysis of 56,223 citations across six answer engines and four international markets, drawn from July and August 2025 data, found 66.0% of citations came from global, mostly US-based domains regardless of where the user searched, and only 18.3% from country-code domains that genuinely represent a local market. Its dataset skews towards B2B SaaS and tech brands, many of them US-headquartered, which the authors note inflates the .com share.
Those two findings together explain the shape of the problem. A global corpus means a US vendor is often retrievable everywhere, so you are not invisible abroad by default. A local question, asked in a local language about local requirements, pulls local sources, and that is the moment a vendor with no material written for that market drops out of the answer. Our explainer on how AI engines choose which sources to cite covers the retrieval step in more detail.
Does writing in the local language help, or does location win?
Location usually wins over prompt language, so writing in the local language helps only when the content is also reachable from that market. Translating a page and leaving everything else unchanged is the common mistake.
The evidence for location winning is specific. Evertune reports that ChatGPT prioritises the user's location over the language of the prompt for certain queries, giving an example where a Japanese-language grocery query returned US retailers for a US-based user, while Google AI Overviews returned Japan-specific results for the same question. Two engines, same prompt, different weighting of the same two signals.
What that means in practice is that language is a retrieval cue rather than a switch. A German-language page about data residency gives an engine something to retrieve when a German buyer asks in German about data residency, which the English version cannot do as well. The page earns its place by answering a question that is actually asked in that market, not by existing in that language.
Order the work accordingly. Write the market-specific answer first, in the market's language, and worry about translating the general library later. A translated blog post about what AEO means serves nobody in Berlin, while a German page naming the compliance requirement your buyers must satisfy can be the only source an engine has for that question. Our guide on query fan-out explains why the sub-questions an engine generates, rather than your headline topic, decide what gets retrieved.
Which pages should you localise first if you sell in several countries?
Localise the pages where the honest answer differs by country, starting with the ones a buyer uses to disqualify vendors. Compliance, data residency, currency and pricing structure, delivery or implementation terms, support coverage and local references are the usual list.
Disqualifying questions come first because they are asked early and answered elsewhere if you are silent. A buyer in the EU asking whether a vendor hosts data in the EU gets an answer from somebody, and if your site does not state it, the assistant either omits you or fills the gap from a third party. Putting the answer in plain text on a page is cheap and it is the single most common missing piece we see in multi-market sites.
Proof is second. Local references, local case material and local review profiles are what make a vendor look present rather than merely available, and they are also what local-language sources tend to cite. Our guides on whether you need third-party mentions and whether G2 and Capterra reviews affect AI recommendations cover the off-site half of this.
Resist the urge to duplicate the whole library per market. Six translated versions of one explainer give an engine six mediocre candidates for the same question and none of them clearly best, which is the same cannibalisation problem that classic search has, in a system with less tolerance for near-duplicates. One strong page per genuinely market-specific question is a better use of the same budget.
Currency and pricing structure deserve a specific mention, because they are where a global page is most often unusable locally. Stating what is included, in what currency, and how the contract works in that market is the kind of detail an assistant can quote, and our page on whether you need to publish pricing to get recommended by AI works through how much to say.
How do you measure AI visibility in a market you do not sell from?
Measuring AI visibility in another market needs runs that originate from that market, which your own browser cannot fake reliably. A VPN changes your IP and leaves your account history, language settings and prior context in place, and assistants read more than one signal.
Three approaches work in practice. A tracking tool with country selection runs the prompts from the market you choose: Otterly's documentation on choosing a country for a prompt notes that ChatGPT, Google AI Overviews and Perplexity often adapt answers to the user's location, which is why the setting exists. A colleague, partner or reseller in the market can run an agreed prompt list from a logged-out session and send back the full text. A supplier running your programme should be able to do either on request.
Whichever route you take, hold the method still. Same prompts, same language per market, logged out, same number of runs, logged with dates. Mixing a VPN run this month with a partner's run next month produces a difference you cannot attribute, and given that the FTA Global testing found a consistent top brand in only 35% of repeated tests, a single run per market will mislead you more often than it informs you.
Budget for the multiplication before you promise a report. Four markets at 20 questions across three engines, run four times, is 960 answers somebody has to read, which is why market coverage is one of the biggest drivers of what this work costs. Our guides on how many prompts you need to track and what moves the cost of an AEO quote cover the arithmetic.
What does this change about your question set?
Your question set changes in two ways: it needs a version per market, and each version needs the questions that market actually asks rather than translations of the questions your home market asks. Translation alone measures the wrong thing accurately.
Core product questions do translate. What the product does, who it is for, how it compares to the obvious alternative: these are stable across markets and a translated version of each is a reasonable starting point. The questions that decide a shortlist are the ones that differ, and they are usually about constraints rather than features.
Build each market's set from local evidence. Ask the salespeople who work that territory what the first three objections are, read the local-language terms competitors use for the category, and watch which local publications appear as sources when you run the general questions from that market. Those sources are telling you what the engine considers authoritative there, which is a fast way to learn what to write and where to be mentioned.
Keep the sets versioned and separate in the reporting. One blended global percentage hides exactly the thing you are paying to find out, which is that you hold a strong position in one market and none in the next. A table with a row per market and a column per engine is a page an executive can act on, and our guide to reporting AI search visibility to an executive team covers how to present it without implying more precision than the runs support.
If you want a read on a market before committing budget to it, a free visibility assessment runs your buyer questions across ChatGPT, Claude, Perplexity and Google AI Overviews and reports who is named today, from which page.
What else do people ask about AI search and different countries?
Can you check what AI search says about you in another country?
You can check another country's answers, but not reliably from your own desk with a VPN alone. Assistants infer location from several signals at once, and an account with a history of your own market can carry that context into the answer. The dependable options are a tracking tool that runs prompts from a chosen country, or a colleague or partner in the market running an agreed prompt list from a logged-out session and sending back the full text.
Do you need a separate page for each country to be recommended there?
A separate page per country helps only where the answer genuinely differs by market, which is most often compliance, currency, delivery, support hours and local proof. Duplicating a general explainer into six near-identical localised versions gives an engine six weak candidates and no clear best answer. One strong page per market-specific question beats a translated set that repeats the same content in different words.
Does being a US company help you get recommended everywhere?
Being US-based is an advantage in the retrieval corpus rather than in the market, and it is a real one. Published citation analysis finds global domains, mostly US ones, dominating the sources AI engines cite even for queries made from other countries. What it does not buy is the local answer: when a buyer asks in their own language about local requirements, engines reach for local sources and a US vendor with no local material is not among them.
Should your AI visibility question set be different for each market?
Each market needs its own question set, because buyers in different countries ask about different things in different words. The core product questions translate, and the questions that decide a shortlist usually do not: data residency in Germany, procurement rules in the UK public sector, local payment methods in India. A set that is only a translation of the English one will measure the questions you already knew about and miss the ones costing you deals.
Which engine is most sensitive to the buyer's location?
Published testing suggests Google's AI surfaces localise more readily than ChatGPT, which often defaults to global sources even where local alternatives exist. Treat that as a tendency rather than a rule, because the behaviour differs by category and changes without notice. The practical response is to measure the engines separately per market rather than assuming one pattern holds across all of them.