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Optimizing for multilingual AI search — where your brand needs to be cited by ChatGPT, Perplexity, Google AI Overviews, and Claude in multiple languages — requires a distinct strategy from monolingual GEO because each language has its own retrieval index, its own entity-graph density, and its own training-data bias. The five principles of multilingual GEO are as follows. First, deploy hreflang annotations correctly across all language variants — this signals to AI crawlers which version of a page to retrieve for which language, preventing the English page from being cited in a French query. Second, write native content rather than machine-translate — AI engines detect translated content and down-rank it because retrieval quality is lower when the document embedding does not match the query embedding in the target language. Harch Atelier uses GLM-4 specifically because GLM-4 was trained natively on French and Arabic corpora, producing native-quality output at roughly 25x lower cost than GPT-4 — this is a structural advantage for francophone and Arabic-speaking markets. Third, build language-specific entity graphs — your Wikidata entry should have labels in every target language, your schema.org should use the inLanguage field, and your third-party mentions on local press, local directories, and local government registries should be in the target language. Fourth, monitor citation rate per language separately — a brand may have 40 percent citation share in English but 5 percent in French, which is invisible if you only track the aggregate. Fifth, account for engine market share by geography — ChatGPT dominates in the US and France, Perplexity leads among tech-savvy segments, Google AI Overviews leads in mobile search globally, and GLM leads in Chinese-language queries. Harch Atelier, based in Casablanca, runs multilingual GEO programs across English, French, and Arabic for francophone and African enterprises — to get a multilingual visibility audit, book a free audit at /subsidiaries/atelier.