Multimodal AI training data.
In any language, across every modality.
Welo Data produces image, video, audio, text, and document training data across 155+ locales — with the linguistic and operational depth to run programs where they’re hardest: low-resource languages, specialist domains, and combined modalities under one quality framework.
text, audio, and visual tasks
Six modalities. One delivery team.
Each modality runs independently or combined into a cross-modal program. Same ontology, same delivery team, same quality standards across all data types. That’s where most multi-vendor approaches break down.
Image
Bounding box annotation, polygon segmentation, keypoint labeling, and attribute tagging across diverse image sets — annotated by contributors with the cultural and linguistic context your model will encounter in deployment.
Video
Action recognition, multi-object tracking, temporal event tagging, inferred intent annotation. The hard part isn’t the tooling — it’s annotators making consistent judgment calls across thousands of hours of edge-case footage.
Audio & Speech
Transcription, diarization, acoustic event tagging, emotion annotation — delivered by native speakers of the target language, in the target dialect. Built for ASR systems and audio-language models that have to work outside English.
Explore Voice AI data programs →Text
NER, intent classification, semantic labeling, instruction-response pair generation across 155+ languages. Short-form tasks to long-form domain-specific documents, including languages where contributor quality is genuinely hard to source and verify.
Document & OCR
Layout annotation, OCR, table extraction across scanned and photographed documents — including non-Latin scripts that most providers treat as edge cases. Built for document understanding models that need to work across geographies.
Cross-Modal Pairing
Image-text pair generation, audio-visual alignment, video captioning across paired datasets. Cross-modal semantic consistency is checked before delivery — not discovered at evaluation.
Multimodal data for systems that have to perceive and act in the physical world.
Robotics and autonomous systems programs require more than annotation. Secure lab infrastructure, compliant roster management, multilingual voice and motion data, on-site collection protocols — the operational layer is where these programs succeed or fail.
Welo Data runs end-to-end physical AI data programs: from lab setup and safety compliance through multilingual data collection and structured delivery. The same contributor depth and quality standards apply here as on every other program.
See Robotics & Physical AI →What’s standard here isn’t standard elsewhere.
These aren’t premium add-ons. They’re how every program runs.
Multilingual coverage that goes the full depth
155+ locales across every modality. Audio in the target dialect. Images annotated with cultural context for the target market. Documents processed by script-literate contributors.
Explore multilingual AI capabilities →Domain-credentialed contributors where it matters
Medical, legal, financial, and technical content goes to contributors with validated domain credentials in the relevant field and language. Not generalist workers attempting specialist work.
Cross-modal consistency, enforced
Same ontology and annotation guidelines across all data types in a program. Inconsistency between modalities is one of the main failure modes in multi-vendor programs. It doesn’t happen here because there’s one team and one standard.
Original data collection, fully managed
Contributor sourcing, consent, rights clearance, structured delivery. For programs requiring data generated from scratch rather than existing assets annotated.
See data collection infrastructure →QA with teeth
Inter-annotator agreement scoring, gold task calibration, audit trails. Accuracy thresholds are set before work begins, not negotiated after delivery.
Compliant by design
All original collection includes explicit contributor consent and appropriate licensing. Programs scoped to GDPR, HIPAA, and equivalent frameworks — documented, not assumed.
Delivery formats that don’t require cleanup
JSON, CSV, COCO, PASCAL VOC, custom schemas. Format agreed at scoping. Data arrives structured and ready.
Same infrastructure at any scale
Pilot to production, the same quality controls and team structure apply regardless of volume.
Most programs here combine modalities.
Tell us what you’re building. We’ll scope the right approach.
Talk to Our TeamLanguage is the hard part. We solved it first.
The providers who built for English and added language coverage later show the seams at scale. Welo Data built the other way around — and it changes what’s possible across every modality.
155+ locales means 155+ locales
Not 155+ with English, Spanish, and Mandarin well-covered and everything else best-effort. The same contributor network depth, dialect coverage, and quality infrastructure applies across the full locale set — including the languages where most providers quietly under-deliver.
One team across all modalities
Image, video, audio, text, document. One delivery team, one set of quality standards, one point of accountability. The cross-modal consistency problems that come from multi-vendor structures don’t arise here because there isn’t one.
Cross-modal QA before it reaches you
Paired data — image-text, audio-visual, video-caption — is checked for semantic consistency before delivery. The error surfaces at our QA stage, not yours.
Specialist content handled by specialists
Medical imaging annotated by contributors with validated medical credentials. Legal documents reviewed by contributors with legal domain knowledge, in the target language. Not a common capability.
See how agentic programs use the same contributor depth →The infrastructure to run it at scale
Welo Data’s contributor network and program infrastructure have operated at production scale across languages and data types for over 20 years. That matters when a program has to run without the wheels coming off.
The clients who needed to know it works
Google, OpenAI, Meta, Apple, and Anthropic use Welo Data for programs where data quality and linguistic precision are non-negotiable.
“The realism of generative AI models is increasingly reliant on trusted, high-quality human feedback. Welo Data’s deep expertise across languages and data types delivers the trusted data at scale needed to realize the promise of generative AI.”Professor Larry Carin — Duke University (Emeritus)
Questions worth asking. Straight answers.
How do you enforce annotation consistency when a program spans three modalities and four locales simultaneously?
What happens when IAA scores fall below threshold mid-program?
How does language coverage actually work for non-text modalities?
What does original collection cover, and who owns compliance?
How do you verify cross-modal alignment before delivery?
Can you run a robotics or physical AI data program end to end?
If the data layer fails, the model fails.
Tell us what you’re building. We’ll tell you how we’d run it.
Contact Us Today →