AI Search Readiness Works for Any Language and Market — Here's How

I live in Portugal and build English-language tools. My site runs on a .com domain, my content is in English, my audience is global, and my server sits in Germany. Nothing about this setup is unusual in Europe. Most businesses I interact with here operate across at least two languages. Many operate across four or five.

When I built a scanner that evaluates how well sites are prepared for AI search, I did not build it for English-only sites. I built it because I kept scanning Portuguese, German, and Dutch e-commerce sites and seeing the same structural problems over and over. Missing schema markup. JavaScript-dependent rendering. No hreflang tags. Broken canonical chains across language versions.

These problems are not language problems. They are engineering problems. And they show up identically whether the site is in Portuguese or English. This article explains what I have learned from scanning European sites across multiple languages — and what multilingual sites specifically need to watch for.

A Caveat Before We Start

I want to be upfront about something. I ran a study across 441 domains to see whether higher structural readiness scores correlate with more AI citations. The correlation was essentially zero (r=0.009, p=0.849). Fixing your schema markup and hreflang tags does not guarantee that ChatGPT or Perplexity will start citing you.

What I did find is that content relevance is the real gate. Sites were 62 times more likely to be cited when the query matched their actual topic. A dive shop gets cited for diving questions, not for general e-commerce questions — regardless of its readiness score.

So why does structural readiness still matter? Because when AI engines do crawl your site for a relevant query, you need to be machine-readable. Broken rendering, missing structured data, and blocked crawlers mean the engine cannot extract your content even when it wants to. Structural readiness is necessary but not sufficient. Keep that distinction in mind as you read.

Why Technical AI Readiness Is Language-Agnostic

The 26 checks in the AI Search Readiness Score evaluate technical signals, not linguistic quality. I designed them this way deliberately, because the problems I see on European sites are structural, not linguistic.

Schema.org Works the Same Everywhere

JSON-LD property names (name, description, offers, priceCurrency) are identical whether the values are in English, Portuguese, or Finnish. When I scan a Portuguese product page with a name in Portuguese and a priceCurrency of "EUR," the structured data is processed the same way as an English product priced in USD.

Checks MR3 (Schema.org Structured Data), TR6 (GTIN/MPN), OR5 (Price & Currency), and OR6 (Breadcrumbs) all evaluate the presence and completeness of structured data — not the language of its values.

Crawl Access Is Protocol, Not Language

Robots.txt uses User-agent, Allow, and Disallow directives that are language-independent by definition. Check MR1 verifies whether AI crawlers like GPTBot and PerplexityBot can access your content. This works identically on a .pt domain, a .de domain, or a .com domain.

MR4 (SSL/HTTPS) and MR6 (Canonical URL) are pure infrastructure checks. SSL certificates do not have a language property. Canonical tags use URLs, which are language-neutral strings.

Meta Tags Work in Any Character Set

Open Graph tags and HTML meta tags work in any encoding. Checks MR5 (Open Graph) and MR14 (Page Title & Social Meta Tags) evaluate whether these tags exist and contain meaningful content — not whether that content is in a specific language. A well-formed og:title in Arabic scores the same as one in English.

Playwright Renders Any Content

The scanner uses Playwright to render pages exactly as a real browser would. Right-to-left languages, CJK characters, Cyrillic — all render correctly. Check MR7 (JS Rendering) compares static HTML word count to rendered word count. This comparison works in any language because it measures content visibility, not content meaning.

Content Structure Is Structural, Not Linguistic

Checks like EX6 (Heading Hierarchy), EX7 (Content Depth), EX3 (FAQ Content), and EX12 (Rich Content) evaluate structure — whether headings follow a logical hierarchy, whether the page has sufficient depth, whether FAQ patterns exist in the markup. A 1,200-word Portuguese product page with proper H1/H2 hierarchy scores the same as a 1,200-word English one.

The 4 LLM Checks: Multilingual by Design

Four checks use GPT-4o to evaluate content quality signals that rule-based checks cannot capture:

• EX_LLM_BLUF — Content Clarity (BLUF/TL;DR): Does the page lead with its key conclusion?
• EX_LLM_FAQ — FAQ Content Richness: Are FAQ answers substantive and specific?
• EX_LLM_LOCAL — Local Relevance: Does the content demonstrate local market knowledge?
• EX_LLM_STRUCTURE — Content Structure: Is the page organized for easy extraction?

GPT-4o supports 90+ languages. The scoring engine sends page content for evaluation without translating it to English first. The LLM evaluates clarity, structure, and local relevance in the page's own language. A well-structured FAQ section in German scores just as highly as one in English.

The EX_LLM_LOCAL check is where I see the biggest gap on European sites. Many sites are machine-translated from English with no local adaptation. A Portuguese dive shop that mentions local water temperatures and Portuguese certification requirements scores higher than a generic translation. The LLM can tell the difference, because the difference is real.

What I See Scanning European Sites

After scanning sites across Portugal, Germany, the Netherlands, and other European markets, certain patterns keep repeating. These are not hypothetical problems — I see them on real sites every week.

Mixed-language pages are everywhere. Navigation in English, product descriptions in Portuguese, reviews in a third language. I see this constantly on Portuguese e-commerce sites that imported their catalog from an international supplier without translating the product data. AI engines struggle to determine the page's language and target market when three languages coexist on one page.

Schema markup is missing more often than in English-language sites. This might be a tooling gap — many popular e-commerce platforms default to English-centric SEO plugins that site owners in other markets never configure. The schema itself is language-neutral, but the implementation step gets skipped.

JavaScript rendering is a bigger problem on smaller markets. Smaller European e-commerce sites tend to use template-heavy platforms where most product content loads client-side. When I compare static HTML to rendered HTML, the static version often has fewer than 50 words. That triggers a severe penalty in our scoring because AI crawlers may not execute JavaScript at all.

Hreflang is either absent or broken. Sites that serve multiple language versions frequently get hreflang wrong — pointing both versions to the same canonical, using incorrect locale codes, or simply omitting the tags entirely.

Multilingual Sites: What to Watch For

If your site serves content in multiple languages, these are the specific areas where I see things go wrong most often.

Use hreflang Tags Correctly

Hreflang tags tell AI engines which language version of a page to cite for which query. If someone asks Perplexity a question in Portuguese, and your site has both /en/ and /pt/ versions, hreflang helps the engine cite the Portuguese version. Without hreflang, the AI may cite the wrong language version — or skip your site entirely because of the ambiguity.

Set Canonical URLs Per Language

Each language version needs its own canonical URL. If /en/product-x and /pt/product-x both point to the same canonical, you create a duplicate content signal that confuses AI indexing. I see this mistake on at least a third of the multilingual sites I scan.

Stop Mixing Languages on a Single Page

This is the most common problem I encounter on Portuguese e-commerce sites. The site template is in Portuguese, but product descriptions were imported in English from the manufacturer. Or the reviews are in three different languages because the review platform does not filter by locale. AI engines trying to determine the page's language see conflicting signals and may deprioritize the page entirely.

Scan Each Language Version Separately

Each URL is scored independently. If you have example.com/en/ and example.com/pt/, scan both. I regularly see cases where the English version scores 60+ while the Portuguese version scores below 30 — because the Portuguese pages are missing FAQ sections, have thinner content, or lack schema markup that the English version has. The gap tells you where to invest.

Use Schema.org inLanguage Property

Schema.org supports an inLanguage property that explicitly declares the content language. Adding "inLanguage": "pt-PT" to your Product or WebPage schema helps AI engines correctly classify your content. This is especially valuable when your domain TLD does not match your content language — a .com domain serving Portuguese content, which is exactly my situation.

Try It on Your Site

The scanner works with any language and any market. I built it that way because I needed it to work that way — scanning Portuguese sites was my starting point, not an afterthought.

Will a high score guarantee AI citations? No. My own research showed that it does not. But it will tell you whether your site is machine-readable, properly structured, and not actively blocking AI crawlers. Those are prerequisites, not guarantees.

Run a free scan and see where your site stands. It takes under two minutes and works in any language.