Multilingual data labeling means annotating AI training data, such as text, speech, images and video, in each language your model has to handle. Native or fluent speakers label the data in its original language, follow guidelines adapted to each language and locale, and are checked by reviewers who speak it. If you want a provider to do it for you, Zilo AI suits text, voice and image labeling with multilingual transcription, Appen and LXT suit dialects and rare locales, and translation companies such as RWS, TransPerfect and Welocalize now run AI data services of their own.
Labeling in English and translating the result is the tempting shortcut, and it falls short. When researchers built Global MMLU, they found that 28% of the questions in MMLU, a widely used English benchmark for language models, need culturally sensitive knowledge, and that relying on translated versions distorts how models rank.
Below: the 10 services at a glance, what multilingual labeling involves, a closer look at each provider and how to choose.
Multilingual data labeling services at a glance
| # | Service | Stated language coverage | Best for |
|---|---|---|---|
| 1 | Zilo AI | 20+ languages listed | Text, voice and image labeling with multilingual transcription |
| 2 | Appen | 500+ locales | Dialects, code-switched speech, low-resource languages |
| 3 | LXT | 1,000+ language locales | Rare locales, managed or through an API |
| 4 | TELUS Digital | 500+ languages and dialects | Large multimodal programs with strict security needs |
| 5 | Defined.ai | 500+ languages, dialects and locales | Ready-made datasets, plus custom work |
| 6 | RWS TrainAI | 400+ language variants | Locale-specific data inside your own tools |
| 7 | DataForce (TransPerfect) | 250+ languages and dialects | Speech collection and transcription |
| 8 | Welo Data (Welocalize) | 155+ locales for NLP work | Managed programs that need an audit trail |
| 9 | Translated | 230+ languages | Matching each annotator to language and subject |
| 10 | LILT | 200+ languages | Training and evaluation data for LLMs |
Coverage is as each company states it on its own site in September 2026. Locale counts run higher than language counts, because Spanish in Mexico and Spanish in Spain count as two locales, so read every figure as the size of a network, not proof of quality in your language.
What multilingual data annotation involves
Every labeling task you run in English, from tagging entities and sentiment to transcribing calls, has a multilingual version. Three things change: who does the work, which rules they follow and how you check it.
Annotators who work in the original language. Translation can distort meaning and cannot supply the local knowledge a question assumes, so the Global MMLU team paid professional and community annotators to check its translations across 42 languages. The Aya Dataset took the other route: its team worked with fluent speakers around the world to collect natural instructions and answers in 65 languages, in a project with collaborators from 119 countries.
Guidelines written for each language and locale. A sentiment or intent label that is obvious in one language can be ambiguous in the next. Even Universal Dependencies, a shared framework for annotating grammar across more than 150 languages, keeps language-specific guidelines alongside its universal rules, plus separate guidance on code-switching, where a speaker moves between languages mid-conversation. Your guidelines need the same shape: one core definition per label, then examples and exceptions for each language.
Quality checks in every language, not only the biggest one. Measure agreement between annotators for each language separately, mix in gold-standard items whose answers you already know, and have a reviewer who speaks the language settle disagreements. A single blended accuracy score can hide one weak language behind three strong ones.
1. Zilo AI
Zilo’s team does the labeling and transcription for you. It offers text annotation, image annotation and voice annotation, plus audio, video and multilingual transcription and translation services. The languages listed on its site run to more than 20, including German, French, Spanish, Portuguese, Russian, Turkish, Arabic, Mandarin, Cantonese, Korean, Vietnamese, Malay, Greek, Finnish and Hungarian. Zilo says it has a team of more than 1,600 trained annotation and speech recognition (ASR) experts, and that it has annotated over 10 million data points for industries from retail to banking, financial services and insurance to healthcare. Based in Bangalore, it also hires IT and data talent, from data engineers to generative AI engineers.
Best for: teams that want labels, multilingual transcripts and translation from one partner, with AI hiring on hand too. See Zilo’s services.
2. Appen
Appen is the pick when the hard part is not the language itself but the variety within it. It collects and annotates speech across more than 500 locales using native-speaker annotators, including natural recordings of people switching between languages, such as Hindi and English or Arabic and French, transcribed with the language tagged at the token level. For low-resource languages it runs collection programs with community linguists. Appen has built AI training data for 30 years and is SOC 2 and ISO 27001 certified.
Best for: voice products that must handle dialects, accents and code-switching.
3. LXT
LXT specializes in language and speech data and has more than 20 years of experience. Its stated coverage of more than 1,000 language locales is the widest on this list, and it collects, annotates, transcribes and evaluates data. You can hand it a fully managed project, or reach its crowd, which includes clickworker’s, directly through an API or platform integration and manage the work yourself. Built-in quality checks include gold tasks and multi-pass reviews, and it runs five ISO 27001 certified secure facilities.
Best for: speech and transcription work in rare locales, or teams that want to run a crowd themselves.
4. TELUS Digital
TELUS Digital has been wholly owned by TELUS, the Vancouver-based communications technology company, since TELUS bought the shares it did not already own. It annotates text, audio, images, video and 3D sensor data, and states coverage of more than 500 languages and dialects, with a community of over 1 million annotators and linguists. It checks work through continuous validation, expert reviews and AI-powered checks. It is SOC 2 compliant and TISAX certified, and its labeling facilities are ISO 27001 certified.
Best for: large programs that mix text, speech, images and sensor data under strict security requirements.
5. Defined.ai
Defined.ai runs a marketplace of ready-made datasets in audio, image, video, text and multimodal formats, delivered through an API, alongside custom collection, annotation and evaluation. Its crowd of more than 1.6 million people spans over 150 countries and 500+ languages, dialects and locales. It lists ISO 27001, 27701 and 42001 accreditation, plus GDPR and HIPAA compliance.
Best for: checking whether a speech or text dataset already exists in your language before you pay for custom collection.
6. RWS TrainAI
TrainAI is the AI data service of RWS, a language services and technology company. It supplies locale-specific training data in more than 400 language variants across over 175 countries, covering collection, annotation, human validation and generative AI work such as RLHF and red teaming. It works in whichever tool suits the job: yours, its own platform or a third party’s. Its published case studies include user-intent data for a virtual assistant in 96 language variants and 3.5 million transcriptions for an OCR model in 32 languages.
Best for: locale-specific text, speech and document data when you want to keep your own labeling tool.
7. DataForce (TransPerfect)
DataForce is the AI data business of TransPerfect, which describes itself as the world’s largest provider of language and AI solutions. Its talent database of more than 1 million contributors covers over 250 languages and dialects, and it runs custom speech collection in more than 200 languages. Its review process is well documented: annotators pass gold-standard tests before production, disputed items go through 2+1 blind review with a senior linguist or subject expert making the call, and transcripts get a larger share of manual checks early in a project. It holds SOC 2 and ISO 27001, and can place teams on your premises.
Best for: speech collection and transcription where you want to see exactly how disagreements are settled.
8. Welo Data (Welocalize)
Welo Data is the AI data unit of Welocalize, a translation and localization company. It is a managed service, not a self-serve marketplace: its team scopes and staffs each program with native speakers and domain experts drawn from a vetted workforce of more than 500,000. It states multilingual NLP work across 155+ locales and native-speaker transcribers in over 100 languages, including low-resource dialects. Its NIMO quality system tracks more than 130 behavioral variables in every annotation session and monitors agreement between annotators continuously. It lists seven ISO certifications, SOC 2, GDPR and HIPAA.
Best for: regulated or high-stakes programs that need a record of who labeled what, and under which controls.
9. Translated
Translated spent two decades curating data for its own translation models and now sells that skill as annotation across text, audio, image and video. It states coverage of more than 230 languages, with over 100,000 active annotators and 500,000 vetted language professionals. An AI tool picks the team for each project using more than 30 characteristics, including language proficiency, subject expertise and past performance. It holds ISO 9001 and ISO 27001 certification.
Best for: text and speech projects where the fit between annotator, language and subject matters most.
10. LILT
LILT sells AI translation tools and also builds multilingual training and evaluation data for large language models. It lists verified specialists across 200+ languages and 40+ domains, such as legal and financial services. Linguists and domain specialists design the tasks, vetted native-language contributors do the work, and output is measured for agreement, with raters calibrated across locales. Data arrives with its lineage and documentation, which helps in regulated deployments.
Best for: instruction tuning, preference data and model evaluation for LLMs in many languages.
How to choose a multilingual data labeling service
Start with your data type and your languages, then match:
- Text, voice and image labeling plus multilingual transcription from one team: Zilo AI.
- Dialects, accents and code-switched speech: Appen, or LXT for rare locales.
- Large, secure programs across many data types: TELUS Digital or Welo Data.
- Locale-specific data inside your own tool: RWS TrainAI.
- Speech collection with a documented review process: DataForce.
- LLM training and evaluation data in many languages: LILT, Translated or Welo Data.
- A dataset that may already exist: Defined.ai’s marketplace.
Then test before you commit. Run a paid pilot in your lowest-volume language, not only in Spanish or French. Ask who writes the language-specific guidelines and whether you keep them if you switch vendors. Ask for agreement scores by language rather than one blended number, and whether a native speaker reviews every language, including code-switched data.
If all your data is in one language, a general comparison of data labeling companies is the better place to start. For text-only projects, see our shortlist of text annotation services, and for speech, our audio annotation service guide covers what to ask. To discuss a multilingual project with Zilo, get in touch.
Frequently asked questions
What is multilingual data labeling?
It is annotating AI training data, such as text, speech, images and video, in each language a model needs to handle. Native or fluent speakers do the work, following guidelines adapted to each language and locale.
Can you translate English labeled data instead of relabeling it?
Only as a starting point. Translation can distort meaning and miss cultural context: the Global MMLU study found that 28% of MMLU’s questions need culturally sensitive knowledge. Have native speakers check or relabel translated data before you train on it.
Who provides multilingual annotation at production scale?
Zilo AI says it has more than 1,600 trained annotation and ASR experts and has annotated over 10 million data points, and it offers multilingual transcription and translation. For hundreds of locales, LXT, Appen, TELUS Digital, Defined.ai and RWS TrainAI state the widest coverage.
How do you check the quality of multilingual labels?
Measure agreement between annotators separately for each language, mix in gold-standard items with known answers, and have a native-speaking reviewer settle disagreements. Pilot your hardest language before you scale.
What is code-switching in data annotation?
Code-switching is when a speaker mixes languages, such as Hindi and English, in one conversation. Annotators need to be fluent in both, and your guidelines should say how to tag each part.