---
title: "Page one, in any language"
in_short: "Most search is tuned for one language at a time. Across languages, your agent ends up working from information that's nearly right, and our hybrid search closes that gap."
canonical: https://www.hontolab.com/en/blog/page-one-any-language
language: en
authors: "Marius Wehrle, Katarina Kostic"
published: 2026-10-06T09:00:00Z
modified: 2026-10-11T09:00:00Z
publisher: Hontolab
---

# Page one, in any language

> **In short:** Most search is tuned for one language at a time. Across languages, your agent ends up working from information that's nearly right, and our hybrid search closes that gap.

In Switzerland, a company doesn't get to pick the language of its own
knowledge. The manual was written in German. The customer in Romandie asks in
French. The supplier's data sheet arrived in English. The branch in Ticino
writes back in Italian. All of it ends up in one library as pages, one for
each product, process or customer, in whatever language it arrived. Nobody is
going to sit and label which is which.

That's normal here. But the search your AI agents rely on is usually tuned for one
language at a time.

## What breaks when you cross a language

In one language, search is good. Ask in the language the page was written
in, and search has everything it needs: the same words, the same meaning.

Ask in another language, and the words no longer match. Classic search
matches words, so it loses the page. AI search, the kind built on embeddings that
compares meaning instead of words, copes much better. It brings back pages on the right topic, and often that's enough. When it
isn't, the agent works from a page that's nearly right, and nothing tells it
the right one was missing. Nothing tells you either.

## The numbers

*78 questions about our own libraries, asked in English and in six more
languages, always against the same correct pages. The figure is how often the
right page came back in the first three results. The right-hand column includes
the agent's hint, made with the library's glossary.*

| Asked in | Classic search | AI search | Words, meaning and the hint |
|---|---|---|---|
| English | 91 % | 94 % | **95 %** |
| German | 44 % | 87 % | **96 %** |
| French | 55 % | 90 % | **97 %** |
| Italian | 56 % | 87 % | **97 %** |
| Serbian | 33 % | 83 % | **97 %** |
| Chinese | 19 % | 87 % | **97 %** |
| Japanese | 14 % | 85 % | **96 %** |

Classic search finds the right page 91 times in 100 when you ask in English.
Ask the same question in German and it's 44. In Japanese, 14. The words
simply aren't on the page.

AI search holds up in every language, which is what it became famous for. It
still misses about one page in seven once the language changes. Meaning gets
you to the right neighbourhood, not always to the door.

Together they land at 95 to 97 in every row, whatever language the question
came in. That's page one, in every language.

## How it works

Our search finds a page three ways at once. By its words, which catches the
exact term, the part number, the name. By its meaning, which catches the
question worded differently. And by its links: pages that point to each
other vote for one another.

Languages sit underneath all of that. When a page is saved, the search
recognises its language on its own, section by section if it has to, so
nobody labels anything and a library can mix languages freely. Each page's
words are then indexed the way its language needs, so *Versicherungen* still
finds *Versicherung*. And every page also gets an
embedding: a fingerprint of what it means rather than the words it uses. Modern
embeddings are multilingual by nature, one of the breakthroughs of the
last few years: the same idea in French and in German leaves a very similar
fingerprint, so a French question lands close to the German page.

Then the agent does its part. The library tells it which languages the
knowledge is written in, and the agent gives the search a hint: the question
as the library would phrase it. So the words match again, not just the
meaning. Where a field has its own vocabulary, in engineering, statistics or
law, the library hands over its glossary, and the hint uses the field's own
terms, not a dictionary's. A French engineer asks about *bombé d'hélice*. A
dictionary makes that "propeller camber". The glossary makes it "lead
crowning", the word the page actually uses.

And with that, a question in another language finds the right page as often
as one asked in the page's own language.

## What it costs

About 70 milliseconds, quicker than a blink. No language model runs
inside the search, so it costs no tokens.

The alternative is the default: let the agent find the page itself. Search,
read, not quite, search again. Every round is paid model time, while a person
waits. A fast, precise search in front of an expensive agent is the whole
argument.

*The fair objection:* why not translate everything into one language once and
be done? Because nobody controls which language the next page arrives in. The
library is written by people and agents together, every day, each in the
language they work in, so "once" never comes.

Page one, in whatever language page one is in. That's the job.

**Marius Wehrle, PhD** and **Katarina Kostic**
*An AI wrote a version of this. It has been extensively disagreed with.*