Language data for ai
Expert language data for the languages on model fails on.
Nordic and Central European languages for training, evaluation, and alignment of large language models. Curated expert pool. EU sovereign infrastructure. Native compliance with GDPR and EU AI Act.
Seven languages, two regions, one specialty.
Three structural choices that define our service
1 — Expert pool, not crowdsourcing
We work with a curated network of qualified professionals — sworn translators, practising jurists, medical doctors, engineers — selected for both linguistic excellence and verifiable sectoral expertise. NDA-bound. Professionally paid. Identified individually. No anonymous accounts. No offshore aggregation.
2 — Native EU compliance
French-incorporated entity. EU-sovereign hosting. GDPR and EU AI Act compliance integrated by default. Article 10 training data governance documentation provided with every delivery. ISO 27001 certification in progress. Compliance is not an add-on — it is the operational baseline.
3 — Sectoral depth in regulated verticals
Legal, medical, technical, and financial expertise in each of our seven covered languages. Our contributors include bar-registered lawyers, board-certified doctors, credentialed engineers, and qualified financial analysts. The depth that makes the difference on regulated AI use cases.
Why Nordic and Central European language matter for AI.
Mid-resource languages, structural performance gap
Large language model performance degrades visibly when moving from English or French to Swedish, Polish, or Hungarian. This is not a scale problem. It is a data problem. The volumes of high-quality professional text in these languages, particularly in regulated verticals, are orders of magnitude lower than what is available in English.
Scraping cannot fill the gap
Most freely available content in these languages is generalist news, social media, or machine-translated text. Adding more of it does not improve model quality — it often degrades it. Regulated content is rarely freely scrapable, and EU copyright law makes large-scale scraping increasingly risky.
Crowdsourcing cannot fill it either
The qualified contributor population worldwide for Hungarian medical writing or Czech legal annotation is in the low hundreds, not thousands. Crowdsourcing platforms cannot deliver the volumes they claim at the quality required. Inter-annotator agreement on specialised tasks is often unacceptably low.
Ready to discuss your language data needs?
Discovery calls are exploratory. No information about your project is requested before signed NDA.