Why multi-language matters for AI

LLMs respond differently to the same prompt in different languages. A Polish-speaking buyer asking about “najlepsze krypto-licencje dla startupów” gets a different answer set than the English-speaking buyer asking the same question — different sources weighted, different brand positions in the answer.

If your brand only ships in English, you are absent from every non-English prompt. For brands with EU, MENA or LATAM market exposure that is meaningful pipeline.

The AP Education engagement shipped UA, EN and PL on every priority page. The PL version pulls separate citations from Perplexity in PL and ChatGPT querying in PL. None of that surface existed in the English-only version of the site.

Hreflang as the foundation

<link rel="alternate" hreflang="..."> in every page head, covering every language variant including x-default. Example:

<link rel="canonical" href="https://answerly.agency/services/aeo-starter-audit">
<link rel="alternate" hreflang="en" href="https://answerly.agency/services/aeo-starter-audit">
<link rel="alternate" hreflang="uk" href="https://answerly.agency/ua/services/aeo-starter-audit">
<link rel="alternate" hreflang="x-default" href="https://answerly.agency/services/aeo-starter-audit">

Without hreflang, AI extractors treat /services/foo and /ua/services/foo as duplicate content with different language. Authority dilutes; both versions perform worse than they should.

This site (answerly.agency) deploys hreflang on every page out of the BaseLayout — see the head source of any page, the alternates are emitted per-locale automatically.

What machine-translates and what does not

Machine-translate (acceptable):

  • Schema markup (Article, FAQPage, Person property values for non-name fields)
  • Quick Facts table contents
  • Technical labels (form fields, navigation items)
  • Footer boilerplate

Never machine-translate:

  • Hero copy
  • X-is-Y intro
  • H2 questions and direct answers
  • FAQ questions and direct answers
  • Author bios
  • Anything where the brand voice should come through

The reason: machine translation passes the meaning but kills the voice. AI extractors weight content quality alongside structural compliance, and machine-translated copy reads as low-effort. We have measured this — pages with machine-translated marketing copy underperform native-edited versions by 40-60% on citation rate in the secondary language.

The discipline: native editor or bilingual writer per language. The AP Education engagement had separate UA, EN and PL editors on retainer. The translation cost is real but the citation lift in the secondary language pays for it within months.

Per-language prompt cluster

Each language gets its own prompt cluster. The PL prompt cluster for crypto licensing is not a translation of the English prompt cluster — Polish buyers ask different questions because the Polish regulatory landscape is different.

Mining prompts in each language uses the same tools (Searchable, Profound) with language-specific filters. Five seed prompts per language, plus the conversational variants. For Scale or Enterprise engagements with three languages, that is 150–200 tracked prompts total — but spread across three teams (one editor / writer per language).

The llms.txt per language

For a multi-language site, the cleanest pattern: one canonical llms.txt at /llms.txt covering the primary language, plus reference to language variants:

# Brand

> Primary description.

...

## Language versions
- English: https://yourdomain.com/llms.txt (this file)
- Ukrainian: https://yourdomain.com/ua/llms.txt
- Polish: https://yourdomain.com/pl/llms.txt

Each language variant is a complete llms.txt in that language. AI systems can discover and cite either depending on the language of the prompt.

This site does it differently — single llms.txt at /llms.txt with explicit pointers to UA paths inside it. Either pattern is valid.

What you should do for a multi-language brand

Start with two languages, not five. Pick the secondary language with the largest commercial pipeline (often Polish for Ukrainian and Romanian businesses, German for many EU SaaS, Arabic for MENA-targeting brands).

Ship the secondary version of:

  • All five service pages
  • Pricing
  • Methodology
  • Top three blog posts

That is roughly two months of native-editor work and gives you the citation surface in language 2. Expand only after that surface is producing citations.

If you want this implemented end-to-end with native editors per language: that is the Scale tier with explicit multi-language scope. Two languages on Scale, three on Enterprise, four+ on custom.