What was the scope?

A B2B exporter with one English site wanted AI visibility in two additional markets. The engagement covered three languages — English, German, Japanese — across the standard surfaces: Google’s AI system, ChatGPT and Perplexity. The anonymized structure: one shared technical base, three content workstreams, three question sets, one measurement cadence. No client numbers are quoted here; what follows is the shape of the work and the lessons.

What transfers between languages — and what does not?

The technical layer transfers almost completely: crawler access, IndexNow, rendering, schema patterns and log analysis are language-agnostic, so one technical sprint lifted all three versions. The content layer partially transfers: the answer-first patterns and fan-out mapping method carry over, but the questions themselves do not — German buyers and Japanese buyers ask different sub-questions, in different decision orders, and translated briefs consistently miss them. The source-graph layer barely transfers: the directories, review platforms and media that engines cite differ sharply by market, so each language needed its own observed citation list. See multilingual GEO.

What did the rollout sequence look like?

  1. Technical foundation (weeks 1-3): hreflang matrix with bidirectional links and x-default, per-language sitemaps, Organization schema with consistent sameAs across versions, IndexNow for all three.
  2. Entity alignment (weeks 2-4): one canonical brand rendering per language with documented romanization and naming rules — the same entity must be recognizable whether the question is asked in English, German or Japanese. See the bilingual entity guide for the cross-language pattern.
  3. Per-language question sets (weeks 3-5): 50-70 questions per language, sourced from native-language sales conversations — never translated from the English set.
  4. Content sprints (weeks 5-12): answer-first rewrites of the top pages per language, native-written, with market-specific comparison and verification content.
  5. Source-graph work (weeks 6-14): directory and profile claims per market, prioritized by each language’s observed citation list.

What surprised us?

Three things. First, entity fragmentation was worse than expected: the brand appeared under four renderings across the three markets, and engines treated some as different companies — the entity audit became the highest-leverage week of the whole project. Second, engine mix differed by market: the same question set produced meaningfully different citation behavior across engines per language, reinforcing per-engine, per-language reporting rather than global averages. Third, freshness discipline decayed fastest in the non-native languages: the English pages got updated on instinct, the others drifted, and grounding noticed before we did. A shared update calendar fixed it.

What would we do differently?

  • Freeze all three question sets before any content ships; we sequenced them, which delayed the German and Japanese baselines and blurred attribution.
  • Budget native-language review and community work from day one; it was scoped as phase two and became the bottleneck.
  • Track hallucinations per language separately; factual errors in answers appeared in one market months before the others and needed source-side fixes there first.

How was it measured?

Per the open standard: four dimensions (Visibility, Citation Rate, Semantic Coverage, Brand Perception), 0-100, per engine, per language, monthly, screenshot archive. The reporting matrix was 3 languages × 3 engines × 4 dimensions — thirty-six series, which sounds heavy but collapses into one dashboard heatmap. The discipline that mattered most was refusing to average: a strong English score never got to hide a weak Japanese one. The measurement rules are in the measurement standard, and the service shape this walkthrough draws on is international GEO.

The one-sentence lesson: multilingual GEO is one technical project, three content projects, and three source-graph projects — budget it that way and the rollout is calm; pretend it is one project with translations and it is chaos.

Frequently asked questions

Can we just translate our English GEO content?
No. The answer-first patterns transfer, but buyer questions, decision clusters and the sources engines cite differ by market. Translated briefs consistently miss native-language sub-questions, and grounding favors native-quality content.
How do you keep one entity across three languages?
Document canonical name renderings per language (including romanization rules), keep founding date, address and description byte-identical across versions, and verify with a cross-language question test: ask about the company in each language and confirm the engine returns the same entity.
How should multilingual GEO be reported?
Per engine, per language, per dimension — a heatmap of 0-100 scores, never a global average. Averaging lets a strong home market hide a failing expansion market for quarters.