How do I track AI visibility in multiple languages?

Q: Can I just translate my English prompts?

No. Translated prompts read like a foreigner wrote them and miss the phrasing real buyers use. Localize instead: use native category terms, local company-size labels, and phrasings pulled from review sites and forums in that language, while keeping the buyer intent constant so results stay comparable across markets.

Q: Do the AI engines answer differently in different languages?

Yes. Ask the same buyer intent in English versus French and ChatGPT often returns almost non-overlapping brand lists, because it answers each language from that language's sources. Coverage quality also varies by engine: DeepSeek has stronger Chinese grounding, and the engine that dominates one country may not dominate the next.

Q: What should I measure per language-market?

Score each language on its own: visibility (share of that language's prompts where you appear), average rank, the sources cited in that language, and share of voice against the local competitors that surface there. The citation lists are the most actionable, since they show which local-language pages the engines trust.

Question

How do I track AI visibility in multiple languages?

Maciej Grabek · Accepted Answer

Track each target language as its own market. Buyers in French, German, or Japanese type different prompts and get different brands back, so an English-only report misses most of your exposure. Run localized prompts (not just translated) in each language across ChatGPT, Perplexity, Gemini, Claude, and DeepSeek, then measure visibility and citations per language-market separately. Why English-only tracking misses most of your exposure A German buyer does not ask "best accounting software" and mentally translate. They ask "beste Buchhaltungssoftware für kleine Unternehmen" and the engine answers from German-language sources: German review sites, German Reddit and forum threads, German press. That answer often names a completely different set of brands than the English one. If you only track your prompts in English, you are measuring one market and reporting it as if it were all of them. For a company selling in five countries, that is roughly one-fifth of the picture. The gaps hide where they hurt most, in markets where a local competitor already owns the answer and you assumed you were fine because the English scan looked healthy. Localize the prompt, do not translate it The common mistake is running Google Translate over your English prompt list and calling it multi-language coverage. Translated prompts read like a foreigner wrote them, and they miss the phrasing real buyers use. "Small business accounting software" translated word-for-word into French is not what a French buyer types into Perplexity. Build the prompt set per language from how buyers in that market actually phrase the question: Use native product category terms, not calques. French buyers say "logiciel de comptabilité", not a literal rendering of "accounting software". Match local buying context: company-size labels (PME vs SMB vs Mittelstand), regulatory terms, and currency. Pull real phrasings from local review sites, subreddits, and forums in that language. Keep intent constant across languages so results stay comparable: the same job-to-be-done, phrased natively in each. Track visibility and citations per language-market Once you have a native prompt set per language, run the full set through all five engines and score each language-market on its own: Visibility: in what share of that language's prompts does your brand appear at all? Average rank or position when you do appear. Citations: which sources the engine linked, in that language, to justify the answer. Share of voice against the competitors that actually surface in that market, which are often local players you do not track in English. The citation lists matter most. They tell you which German or French pages the engines trust, and that is your to-do list: the local-language content, reviews, and directory listings you need to earn to move the number. Engines are not equally good in every language Coverage quality varies by engine and by language, and the engine that dominates one country is not the one that dominates the next. Perplexity leans hard on cited web sources, so in a language with thin English-style review content it may surface local forums instead. DeepSeek has stronger Chinese-language grounding than most Western engines. Gemini and ChatGPT are broad but still skew toward whatever language has the most training and retrieval data. Do not assume your strongest engine in English is your strongest everywhere. Track the per-engine, per-language grid and let the data tell you where to focus. A concrete example: English vs French, same intent Take one buyer intent, small-business accounting, and ask it natively in two languages. English, to ChatGPT: "What is the best accounting software for a small business?" Typical answer names QuickBooks, Xero, and FreshBooks. French, to ChatGPT: "Quel est le meilleur logiciel de comptabilité pour une PME?" Typical answer leans toward Pennylane, Sage, and Indy, brands built for French accounting rules and the expert-comptable workflow. Same product category, same engine, same day, two almost non-overlapping brand lists. A US vendor watching only the English answer would never see that it is invisible to French buyers, or that Pennylane owns the French answer. Multiply that across German, Spanish, and Japanese and you understand why per-language tracking is the only honest way to measure a multinational's AI visibility. avisibli is the GEO platform that publishes this answer library. Self-references are limited to topics where a tool-based answer is genuinely useful to readers.