Built for the 80%+ of the world that doesn’t think in English.
Native-language training data, annotation, and human evaluation across 155+ locales, so your multilingual AI is as reliable in Hindi, Arabic, and Vietnamese as it is in English.
Get in touch →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.
Your benchmarks are in English.
Your users aren’t.
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.
Read the Global Multilingual Safety Audit →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.
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.
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.
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.
155+ locales.
Ready to mobilize.
Established contributor pools across Western Europe, South Asia, Southeast Asia, Sub-Saharan Africa, and the Middle East.
What enterprise teams are
building with us right now.
A snapshot of active program activity. Not a ceiling of what we can do.
Ready when
you are.
We’ll tell you exactly what we can do and how fast.
From training data to
production monitoring.
Native-language data sourcing
Written, spoken, and multimodal — in the target language. Not translated from English.
Annotation and labeling
Domain-qualified native speakers. Calibrated to your task, not generalist fluency pools.
Human evaluation
90%+ evaluator consensus by locale. Built for your rubrics — not ported from English.
Safety and red-teaming
Native-language adversarial testing. Your safety model should pass Bengali, not just English.
See our safety audit findings →RLHF and preference data
Preference annotation in your production languages, not just the languages your team speaks.
Production monitoring
Multilingual quality issues surface before your users find them — by language, by region.
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.
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.
Common questions. Straight answers.
How quickly can you mobilize contributors for a new language or locale?
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.
How do you ensure quality when we can’t verify the language ourselves?
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 →
How do you handle data security for sensitive AI programs?
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.
Does Welo Data support multimodal data beyond text?
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.
Can Welo Data support ongoing production monitoring, or only pre-launch evaluation?
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.