Multilingual AI SEO

Does AI search optimization actually work in French, German, Spanish, Italian, or Dutch?

AI assistants already answer your readers’ questions by reading pages like yours. But almost everything built to help with that, the guides, the checkers, the benchmarks, was built and tested in English. If your content is not, you are being asked to take a lot on faith.

We took the question seriously enough to test it in public: three published studies, six models, and a per-language policy gated on what actually passed. This page shows you what held, what broke, what we fixed, and exactly what an analysis of your page gives you, so you can answer the question that decides outcomes: is this page done?

Free. No sign-up, no card, five full analyses to start.

Full experience in French, German, Spanish, Italian, and Dutch. Japanese, Korean, and Chinese get the score and structure today, with guidance withheld. Exactly why is below.

The problem, precisely

When a tool says it is multilingual, that is usually a claim about the model underneath: it can read and write your language fluently. It is not a claim that the analysis built on top of that model still means anything in your language. Fluency is cheap. Competence on a specific task, in a specific language, is something you can only know by testing, and almost nobody in this category publishes their tests.

So multilingual teams get left with a bad choice: extrapolate English results to French and hope, or sit the whole thing out. Meanwhile the question your pages face is not going away, and it is the same one your English pages face: when an AI reads this page to answer someone, does the page hold up? Does it cover the concepts a reader needs, at the depth that lets one section answer one question on its own?

That question has a testable answer in any language. What follows is our evidence that we can actually give it to you in yours.

We tried to prove AI breaks on your language. We failed, five times.

This month we published the test that matters most for this page. We took six models, including the one behind our English analyses, and tried to prove that their competence on a strict production task collapses outside English. We expected it would. We built three controls to prove it: each model measured against its own English self, every failure re-run to separate real faults from flaky ones, and the same pages run in English, French, and German.

The collapse never came. What came instead was more interesting: five separate results that looked exactly like language effects, and every one of them dissolved when a control touched it.

Two things follow, and they are the two things a multilingual team needs to know. First, the language cliff everyone assumes is not where the risk is: models that can hold this task hold it in French and German too, and models that cannot, fail in English as well. Second, none of this can be eyeballed. Every one of those five false alarms looked completely convincing until a control killed it. Any tool making per-language claims without published controls is guessing, including us, which is why we run the controls.

That is what “engine chosen on evidence” means here: English moved to an open-weight model the day it cleared seven pre-registered checks and a clean week in production. Every other language stayed on the reference model, because nothing else passed. Read the Model Language Transfer Study.

Reading your language was never the problem. Our score was.

A year of multilingual claims can hide one broken number. Ours did. When we first measured the tool on non-English content, reviewers fluent in each language confirmed the analysis understood the pages at or above the English baseline. The reported score still came back lower, and the cause was one scoring step: the analysis wrote concept names in English, then looked for them in your text.

We published the bug with the numbers on it, then shipped the fix: concept labels stay in the source language, and the matching handles accents and inflections. The map you get on a French page is labelled in French because of that bug report. Read the Language Study.

The framework itself is not an English writing convention

There was one more way this could quietly fail. The five structural signals the analysis checks, where a concept belongs, how well it is integrated, how deeply it is developed, what role the page plays, which reader questions it serves, were defined by people writing in English. If they turn out to be English rhetoric habits, the analysis is a category error in German. So we tested that too, on 40 pages written natively for readers of eight languages, translations excluded.

Every language-by-concept verdict came back go, including Japanese, Korean, and Chinese. The framework travels; what varies by language is how much of the product experience has cleared our gates, which is the honesty section below. Read the Concept Validity Study.

What the analysis looks like on a real French page

Here is the whole loop on a live page, not a mockup: the tool reads the page in French, maps what it actually covers, judges how well each concept is integrated, and hands back a decision you can act on.

With it comes the coverage score and its band, so you can tell a page that needs work from a page that is done. Not a promise about rankings. A structural verdict: what is covered, what is thin, what specifically to write next, with the concepts named in your language.

What we can and cannot do, exactly

Run it on your own page

Paste a URL in French, German, Spanish, Italian, or Dutch. About two minutes later you have the concept map in your language, the coverage score and band, the keep-or-split call, and the prioritized writing guidance, the same walkthrough you just watched, on your page.

Free. No sign-up, no card, five full analyses to start.

Common questions

1. Does ContentGrapher work for French, German, Spanish, Italian, and Dutch content?

   Yes. Those five languages get the full analysis: the concept map, the coverage score and band, the keep-or-split decision, and the writing guidance, the same surface an English page gets. The concept labels come back in the source language, not translated into English.

2. Is multilingual AI SEO different from English AI SEO?

   The structural question is identical: when an AI reads your page, can it answer a reader’s question from what is actually there? What differs is verification. Most tools cannot show you evidence that their analysis holds outside English, so ask any vendor for theirs. Ours is published: three studies covering whether the reading holds, whether the framework is an English convention, and how the engines behave across languages.

3. Do you translate my page before analyzing it?

   No. The tool reads your page in its original language. The analysis is written in English and the concept labels stay in the source language, so nothing about your content is flattened into English before it is read.

4. Which model reads my page?

   Non-English pages run on our reference model. We tested six candidate engines for this work and none of them cleared our quality bar, so non-English content stays on the model we trust. English analyses run on an open-weight model that passed seven pre-registered checks and a week of clean production monitoring.

5. Does it work for Japanese, Korean, or Chinese?

   Partly, and we are upfront about it. You get the coverage score and the concept map, but the writing guidance is withheld. It did not clear the quality gate we set before testing, so rather than ship guidance we cannot stand behind, we hold it back until it passes.

6. Why is the writing guidance withheld for Japanese but not French?

   Because French, German, Spanish, Italian, and Dutch passed our validation, and Japanese, Korean, and Chinese have not yet. We gate each language on evidence, not on hope. When the guidance for a language clears the bar, it turns on.

7. Can you guarantee my French page will show up in AI search?

   No, and you should be wary of any tool that says it can. What we do is show you whether your page is structurally complete: whether it covers the right concepts, at the right depth, to answer the questions a reader would ask. That is the part you can control, and the part we can measure.