Why translation and local SEO alone may not reproduce the evidence networks generative engines use in each market.
Language can reconstruct the evidence ecosystem
Chen and colleagues found large cross-language differences in cited-domain overlap. Claude reused English authority domains more heavily in the tested setting, while ChatGPT showed much lower domain reuse; Perplexity and Gemini were between those extremes.
Brand overlap can exceed source overlap
Different language versions may recommend similar major brands while supporting them through entirely different domains. Similar output entities do not imply a shared evidence path.
International GEO therefore needs global authority, local-language authority, and localized third-party evidence—not website translation alone.
Local search is its own GEO problem
Chen’s local-category comparisons found low Google–AI source overlap, ranging from about 20.6% for home cleaning to 0.1% for IT support in the reported sample. Exact figures are snapshot-specific, but fragmentation is strategically important.
The surrounding local ecosystem must describe the business
Business profiles, reviews, directories, location pages, local publications, neighborhood guides, community discussions, local experts, and regional institutions can all supply evidence.
Local visibility depends on whether the local information ecosystem knows what the business is, where it operates, and when to recommend it.
Questions about this topic
Is translating the company website enough for multilingual GEO?+
No. Local-language third-party evidence and region-specific authority sources may also be necessary.
Why can brand overlap exceed domain overlap?+
Different evidence ecosystems can still lead engines to recommend the same established brands.
Is local GEO just national GEO at smaller scale?+
No. Local prompts draw on fragmented geographic sources, reviews, profiles, directories, and community evidence.
What should a local source audit include?+
Official business facts, profiles, local publications, reviews, directories, community sources, experts, and regional institutions.
References
Sources are listed in APA 7 style. Preprints are identified as such and should not be treated as peer-reviewed findings unless separately published.
- Chen, M., Wang, X., Chen, K., & Koudas, N. (2025). Generative engine optimization: How to dominate AI search [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2509.08919
- Martinez, O. (2026). Optimizing visibility in generative engines: A critical survey of generative engine optimization (2023–2026) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2607.14035
- Google Search Central. (2026). Optimizing your website for generative AI features on Google Search. https://developers.google.com/search/docs/fundamentals/ai-optimization-guide
- Kumar, P. (2026). Generative engine optimization at scale: Measuring brand visibility across AI search engines [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2606.20065