155+
Locales
Established contributor pools across 155+ language-locale pairs in 8 global regions. Not just major market coverage.
90%+
Evaluator consensus
Calibrated human judgment across languages. Not just available headcount.
14+
Secure facilities
North America, Europe, Asia, and MENA. Enterprise-grade data security across every region we operate in.
0
Security incidents
Enterprise-grade data handling for sensitive AI programs.
8+
Global regions
Western & Eastern Europe, MENA, South Asia, APAC, Southeast Asia, Sub-Saharan Africa, and Latin America.
There’s a version of your AI that works perfectly. It’s the one that runs on benchmarks, in English, in a controlled environment.
Then there’s the version that meets a user in Lagos typing in Yoruba. Or a customer in Beirut switching between Arabic and French mid-sentence. Or a support query in Guadalajara Spanish that reads as aggressive to a model trained on Castilian.
This is the version most companies haven’t tested.
The gap between benchmark performance and production performance is almost never a model architecture problem. It’s a data problem. Specifically: a multilingual data problem.
WHERE ENTERPRISE AI BREAKS
Your benchmarks are in English.
Your users aren’t.
WELO DATA RESEARCH · 10 LLMS · 79 LANGUAGES
4–5×
higher unsafe completion rates in low-resource languages vs English
79
languages tested across 20 language families
100%
of models tested showed safety degradation in non-English languages
A model that erodes trust in a market you’re entering doesn’t show up on the training budget. It shows up in support tickets, churn rates, regulatory flags, and headlines.
Failure pattern 01 Safety Gap
English guardrails don’t transfer. Our research across 10 LLMs and 79 languages showed 4–5× higher unsafe completion rates in low-resource languages. The exploit is just switching languages. The fix is native-language red-teaming by people who speak the language your users are attacking in.
Failure pattern 02 Training Gap
It ships because speed erodes context first. When pipelines move fast, cultural nuance thins out and decision-making converges. The data still looks complete but it represents fewer ways of thinking. By the time gaps surface in non-English markets, the model is already in production.
Failure pattern 03 Evaluator Gap
Fluency is not enough. Strong outcomes require cultural knowledge, domain expertise, and the cognitive skill for the task. Treating all contributors as interchangeable doesn’t create fairness — it creates inconsistency. We measure for skill and domain fit before contributors ever touch production data.
The cost of inaction What happens if you ship anyway.
By the time it surfaces, it’s a support ticket, a safety incident, or a headline. Nobody asks why the English version was fine. They ask why you shipped something you couldn’t audit.
LANGUAGE COVERAGE
155+ locales.
Ready to mobilize.
Established contributor pools across Western Europe, South Asia, Southeast Asia, Sub-Saharan Africa, and the Middle East.
SOUTH ASIA
Hindi, Bengali, Tamil, Telugu, Kannada, Marathi, Punjabi, Malayalam, Urdu and more
APAC
Japanese, Korean, Mandarin and Cantonese across Mainland China, Hong Kong, Taiwan, Singapore and beyond
SOUTHEAST ASIA
Indonesian, Thai, Vietnamese, Malay, Filipino, Burmese, Khmer, Lao and more
SUB-SAHARAN AFRICA
Swahili, Afrikaans, Bambara and emerging African locales
MIDDLE EAST & NORTH AFRICA
Arabic across 7 countries, Hebrew, Persian, Turkish, Kurdish and more
EASTERN EUROPE
Russian, Polish, Ukrainian, Czech and 20+ additional European locales
WESTERN EUROPE
French, German, Spanish, Italian, Dutch and 25+ locales including Nordic and Iberian variants
LATIN AMERICA
Spanish across 6 markets plus Brazilian Portuguese
CENTRAL ASIA
Kazakh, Armenian, Azerbaijani, Uzbek, Georgian and more
WHERE THE DEMAND IS
What enterprise teams are
building with us right now.
A snapshot of active program activity. Not a ceiling of what we can do.
↑ Active across all regions
Language understanding & generation
Training data and evaluation for LLMs that need to understand and generate in the target language, not translate from English.
↑ Fastest growing request type
Multilingual data for physical AI systems — grounded in how people actually describe space, motion, and instruction across languages and cultures.
Consistent high volume
Human preference & reward data
Preference annotation and RLHF in your production languages. Calibrated to your rubrics by evaluators who think in the language, not through it.
Emerging, growing fast
Domain & cultural specialization
Legal, medical, financial and STEM evaluation in the target language. Also includes safety & alignment evaluation and agentic AI workflows. Fluency alone is not enough for this work.
GET IN TOUCH
Ready when
you are.
We’ll tell you exactly what we can do and how fast.
WHAT WE DO
From training data to
production monitoring.
A snapshot of active program activity. Not a ceiling of what we can do.
01
Native-language data sourcing
Written, spoken, and multimodal — in the target language. Not translated from English.
02
Annotation and labeling
Domain-qualified native speakers. Calibrated to your task, not generalist fluency pools.
03
Human evaluation
90%+ evaluator consensus by locale. Built for your rubrics — not ported from English.
04
Safety and red-teaming
Native-language adversarial testing. Your safety model should pass Bengali, not just English.
05
RLHF and preference data
Preference annotation in your production languages, not just the languages your team speaks.
06
Production monitoring
Multilingual quality issues surface before your users find them — by language, by region.
WHY WELO DATA
What makes us the
obvious choice.
When you need to move fast on a new language or locale, we don’t start building. Our contributor pools are established, qualified, and ready. Because when it matters, you need results, not a roadmap.
01
We evaluate against how people actually talk, not how textbooks say they should
Our 500k+ expert network spans dialects and code-switched varieties that standard benchmarks ignore. We don’t just cover languages. We cover the versions of those languages your users actually use.
02
We catch problems before they reach production
Our multilingual QA pipeline flags where models break down by locale, domain, and demographic. Every gap we identify is a brand incident that didn’t happen.
03
Contributor qualification goes beyond fluency
We test domain accuracy in the target language. A fluency screen doesn’t tell you if someone can evaluate medical content in Telugu or legal text in Indonesian. We do.
04
We make quality auditable, not just asserted
Every pipeline runs through NIMO, our identity verification and quality management system. You get benchmarks, contributor metadata, and anomaly reporting that tells you exactly where your data came from and how it was validated.
Bad multilingual data doesn’t cause one failure. It causes a thousand quiet ones, each eroding trust with a different user, in a different market, in a different way.
FAQ
Common questions. Straight answers.
For established locales within our contributor pool, we can typically mobilize within days, not weeks. For lower-resource or niche locales, timelines depend on contributor qualification requirements. We’ll tell you exactly what we can move on and how fast.
Quality is engineered as an operational layer, not delivered as a promise. Evaluators work from shared calibration standards and decision frameworks before a single judgment is made. 90%+ evaluator consensus across independent native-language contributors is the measurable signal — not a self-assessment. QA runs continuously: golden-set evaluations, real-time error detection, and structured feedback loops that catch drift before it reaches production. Every judgment is traceable and audit-ready. See how our quality systems work →
Welo Data operates 14+ secure facilities across North America, Europe, Asia, and MENA. Air-gapped environments, device controls, and strict data handling protocols are available for programs where data cannot leave a controlled environment. We have zero security incidents across our program history.
Yes. Our contributor pools support multimodal annotation and evaluation across text, audio, image, and video, with the same locale-level depth we apply to text-only programs. For multilingual multimodal work specifically, we handle tasks like audio transcription and translation, image captioning in native languages, and video annotation with locale-specific cultural context. The same qualification and calibration standards apply regardless of modality.
Both. We run pre-launch red-teaming and evaluation programs, and we also support continuous production monitoring for teams that need ongoing signal on model quality across languages after deployment. The same contributor pools and calibration frameworks apply to both.