You dominate your market in Spain, in Germany, in Poland — and ChatGPT still doesn't name you when someone asks in your language, from your country, about your category. It's not your fault: it's how the AI works under the hood. When you ask it in Spanish, ChatGPT doesn't stay in Spanish — it fires off background searches, and a lot of them run in English. Per Peec AI's analysis of over 10 million prompts (February 2026), 43% of those research steps for non-English questions run in English, and in nearly 78% of non-English sessions the AI ends up pulling from English sources. The effect stings: your market leader falls out of the answer and the global brand that dominates English content shows up instead. This guide is the map to stop being invisible in your own language.
The AI asks you in your language, then researches in English
Here's the mechanism almost nobody sees. When you ask ChatGPT a question, it doesn't just answer from memory: it breaks your question into several smaller, more specific searches — the so-called query fan-outs — and goes looking for sources. Peec AI analyzed over 20 million of those fan-outs across more than 10 million prompts (February 2026) and found a clear pattern: the AI starts researching in your language, then switches to English fast. 43% of those research steps for non-English questions ran in English, and in roughly 78% of non-English sessions the model decided the sources in your language weren't enough and topped them up with English research.
Why does it do this? Two reasons, neither random. First: global pages tend to have more links and more citations — exactly the authority signals the model uses to decide who to trust. Second: since about half the content on the web is in English, searching there lowers its risk of not finding something good. The upshot, for you, is that the answer your customer sees is built largely on English content — even though they asked in Spanish.
Why a local leader disappears in its own market
This isn't theory: it happens to brands that own their country. In Peec AI's tests, asking in Polish from a Polish IP for «the best auction portals» returned eBay and global platforms, while Allegro.pl — the undisputed leader of Polish e-commerce — showed up buried or flat-out missing. Asking in German for the best German software companies returned an excellent list… with not a single German company on it. The pattern repeats market by market: a business can be number one at home and be invisible in ChatGPT when the AI goes off to research in English.
The mechanics of the bias are simple once you see them. The AI mixes fan-outs in your language with fan-outs in English. English content shows up in both cases; content in your language only shows up when the search is in your language. That asymmetry, repeated in every answer, pushes systematically toward the brands that dominate English content — almost always the global ones — and against the ones that are only strong in their local market. No bad intent: it's a consequence of training and design. But the effect on your business is real.
| Scenario | What the AI does | Who wins |
|---|---|---|
| Ask in English from an English-speaking country | Researches in English only | Global brand |
| Ask in your language from your country | Mixes your language with English fan-outs | The global one sneaks in; the local one loses weight |
| You're only strong in your language | English content dominates the answer | The competitor with English presence |
The query's language rewrites which sources the AI cites
If the English bias were the whole story, «doing something in English» would fix it. But there's a deeper layer: the language of the query rewrites the entire citation graph. Profound analyzed 3.25 billion citations across 7 models and 14 countries, filtering every prompt by its country's native language — Brazil prompts only if in Portuguese, Japan only if in Japanese (March 2026). Its conclusion: the query's language is the single force that most rewrites which domains appear, how often they're cited and even whether «the social web» shows up in the answer at all.
Translate that to your business: your visibility in English and your visibility in Spanish can be two different universes. ChatGPT naming you when asked in English says nothing about whether it names you when asked in Spanish, or the other way round. Each language is its own board with its own cited sources, its own leaders and its own gaps. That's why the most expensive mistake in multilingual GEO is assuming visibility carries over between languages. It doesn't: you earn it in each one.
What multilingual GEO is (and isn't)
Multilingual GEO isn't translating your site into six languages and crossing your fingers. Brute-force translation is one of the most expensive, least profitable things you can do, because it doesn't touch the real problem: the AI doesn't just look at your site, it looks at third-party sources — listicles, Wikipedia, reviews, media — and often searches for them in English. Translating your domain doesn't get you into those sources. The real work is something else.
- Find out what language the AI researches in for your category. Don't assume it — measure it. In some sectors the answer in your country leans almost entirely on English; in others, your language carries more weight. Knowing this decides where you invest.
- Figure out what English content your global competitors get cited from. If the AI pulls them from a listicle, an international directory or Wikipedia, that's where you're not. Identify which ones matter and which are reachable.
- Decide with data what to have in English too. Maybe an English version of your key product pages pays off, or getting into a couple of international listings for your category. Not everything: what the AI actually cites.
- Reinforce your entity in your own language. A brand that's well described, with schema and consistent data, in your own tongue is still the base for not losing the ground you do control.
The difference between this and translating is the difference between theatre and shipping. Translating is a flashy project that fills a site with little flags and doesn't move a single citation. Multilingual GEO is a data-led investment call: exist where the AI searches for your category, in the language it searches in, without spending on what nobody will cite.
Measure it language by language: visibility doesn't carry over
Measurement is what separates multilingual GEO from faith. And the rule is hard: if you operate in several markets, you have to measure in each language separately, because the same business can rank first in one language and not exist in another. Build the battery of real buying queries for your category — «best [product] for [case]», «alternative to [competitor]», «what do you recommend for [problem]?» — and fire it in every language you care about, from the right country, once a month. Note whether you show up, in what position, with what argument and — this is key — in what language the sources the AI cites are written.
The bias isn't uniform, so prioritizing matters. In Peec AI's data, no non-English language dropped below 60% of sessions with English research: Turkish switched to English in 94% of cases and Spanish in 66% — the lowest, and even so two out of three Spanish searches pass through English content. The more your language leans to English, the more urgent it is to have presence in citable English sources; the less it does, the more it pays to reinforce your language. The data tells you where to press.
If you don't want to build the per-language protocol or read the variance of each market, delegating AI visibility monitoring gives you the clean data per language and the read on where you're strong and where you're invisible; and when it's time to move content and get into where the AI searches, GEO optimization does the work, not the report. For the general frame of what gets measured and how, see the guide on how to measure AI visibility.