If you operate in multiple countries or languages, “your AI visibility” is not one number; it’s a different result in every market, language, and engine combination. Treating it as a single global metric will hide your worst-performing regions. This guide covers how to do AEO across markets.
Why AI answers vary by market
AI engines tailor responses to language and inferred location, and they draw on region-specific sources. The same question asked in English from New York, in German from Berlin, and in Spanish from Mexico City can produce three different answers: different competitors, different cited sources, different framing of your brand. The mechanics behind this are covered in why queries return different results and the geographic variation guide.
The practical consequence: you may dominate your home market and be invisible in a growth market without ever noticing, because you only tested in your own language.
Build per-market prompt sets
The foundation of international AEO is a separate prompt set for each market-language pair, not a translated copy of one. Translation alone misses how people actually phrase questions locally. For each target market:
- Write prompts in the local language, using native phrasing and local terminology.
- Include the local competitors, who are often different from your home-market rivals.
- Add market-specific use cases, regulations, or buying criteria where relevant.
Run each set across the engines that matter in that region. Engine popularity differs by country, so multi-engine monitoring should be weighted toward the assistants your buyers actually use there.
Localize content, don’t just translate
To get cited in a market, the model needs credible, native-language content to draw on. Machine-translated pages rarely earn citations because they read as low-quality and miss local intent. Instead:
- Produce genuinely localized content for high-priority markets: local examples, currency, regulations, and idiomatic phrasing.
- Mirror your home-market AEO structure (direct answers, question headings, FAQs) in each language so the content stays citable.
- Earn local third-party coverage and directory listings, since region-specific sources carry the most weight for region-specific answers.
Manage this through a per-market view of your AEO content calendar so each region has owned coverage rather than leftover translations.
Keep your entity consistent across markets
The hardest international problem is entity fragmentation. If your brand presents inconsistently across languages (different descriptions, categories, or even spellings), engines may treat your local sites as weakly related, splitting your authority. Apply the entity building guide globally:
- State the same core identity (name, category, what you do) in every language, adapted but not contradicted.
- Use hreflang annotations so engines understand that your localized pages are language/region variants of one entity, not separate sites. Google’s guide to localized versions of your pages states the two rules people most often break: every language version must list itself as well as all the others, and the alternate URLs must be fully qualified. A one-way annotation is simply ignored.
- Keep structured data consistent across locales, with the canonical name plus appropriate local aliases.
- Ensure high-trust profiles (Wikidata, LinkedIn, regional directories) agree across markets.
Hreflang and consistent structured data are what let a model connect your German, Japanese, and English presences into a single authoritative entity: the difference between compounding global authority and diluting it.
Prioritize markets, don’t boil the ocean
You can’t fully localize everywhere at once, so sequence by opportunity. Score markets on revenue potential, current AI visibility gap, and competitive intensity, then invest deeply in a few rather than thinly across all. A focused, fully localized presence in three priority markets beats shallow translations in twelve.
Monitor each market separately
Finally, report per market. A drop in your home market and a rise in a growth market can net to “no change” globally while hiding two important stories, so a global average is the wrong unit for an international programme.
Scans do carry a per-market dimension: add each market under Multi-Region Comparison and the same prompts are asked for that market, giving you a per-market visibility score, mention rate and share of voice side by side. That is the granular half, and it is real.
Two mechanics worth knowing before you design the programme around it. The comparison card is the only per-market screen: the Citations page, Rankings, the shared report and the print view all read your default-market scan, and none of them has a market switcher, so per-market citation and position detail is reached through GET /api/v1/scans?region=<market> (or ?region=any) rather than by clicking. And Google AI Overviews treats a region differently from the chat engines: it is a real Google search, so the market travels as an actual search location parameter rather than as an instruction in the prompt, and with no region set, that engine searches as United States. If you are comparing a blank-region project against a UK one, one side is US-localized on that engine and you should know it.
Alerting is not granular per market, and planning around it as if it were will bite you. Alert delivery is a single account-level setting (email, webhook) with no per-market, per-project or per-region scoping, and the messages carry no market label. The cross-scan rules that do exist (a five-point visibility-score move, a ten-point competitor share spike, a newly cited domain, a sentiment shift) compare a scan against the previous scan of the same subject and market, so they work per market in the sense that a German scan is compared to the last German scan, but the notification that results is indistinguishable from any other in your inbox. In practice: let alerts catch the event kinds they catch, and read the per-market comparison yourself on whatever reporting rhythm you keep.
Frequently Asked Questions
Why does AI describe my brand differently in different countries?
AI engines tailor answers to language and inferred location and draw on region-specific sources, so competitors, citations, and framing all shift by market. This is why you should monitor each market-language pair separately rather than relying on a single global visibility number.
Is translating my content enough for international AEO?
No. Machine translation misses local phrasing, intent, and competitors, and rarely earns citations. For priority markets, produce genuinely localized content with native phrasing, local examples, and region-specific sources, mirroring your citable structure in each language.
How does hreflang help with AI visibility?
Hreflang annotations tell engines that your localized pages are language or region variants of one entity rather than separate, unrelated sites. Combined with consistent structured data, this lets a model connect your presences across markets into a single authoritative entity instead of splitting your authority.
Should I target every market at once?
No; sequence by opportunity. Score markets on revenue potential, visibility gap, and competition, then fully localize a few priority markets rather than spreading thin translations across many. Deep coverage in a handful of regions outperforms shallow coverage everywhere.