MULTIMODAL DATA SERVICES
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.
155+
Locales available across every modality, not just text
500k+
Verified contributors across
text, audio, and visual tasks
20+
Years running data programs at production scale for frontier labs
WHAT WE COVER
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.
01
Image
The foundational layer for vision models that need to work in the real world
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.
Bounding boxes
Segmentation
Keypoints
Classification
02
Video
Frame-level precision for models that must interpret what is happening and what is about to happen
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.
Action recognition
Object tracking
Temporal labeling
Event tagging
03
Audio & Speech
155+ locales. Not 155+ with heavy English bias.
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 →
Transcription
Diarization
Emotion annotation
Dialect ID
04
Text
The full language range — not just the easy languages
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.
NER
Sentiment
Intent classification
Instruction pairs
05
Document & OCR
Multi-script. Layout-aware. Delivered clean.
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.
Layout annotation
OCR
Table extraction
Multi-script
06
Cross-Modal Pairing
The data layer vision-language models don’t get right by accident
Image-text pair generation, audio-visual alignment, video captioning across paired datasets. Cross-modal semantic consistency is checked before delivery — not discovered at evaluation.
Image-text pairs
Audio-visual sync
Video captioning
VLM alignment
PHYSICAL AI & ROBOTICS
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.
WHAT WE COVER
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.
WHY WELO DATA
Language 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.
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
The world’s leading frontier labs and Mag-7 technology companies 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)
FAQ
Questions worth asking. Straight answers.
Single delivery team, single ontology. The same annotation guidelines and quality standards are applied across all modalities in a program — not managed separately by modality. Locale-specific guidelines are nested within the master ontology, not run in parallel. When a program combines image, audio, and text across multiple languages, there’s one QA framework that covers all of it.
Work stops on the affected task type. Contributors are recalibrated against gold tasks before resuming. If the issue is systemic — a guideline gap, an ambiguous edge case — the ontology is updated and the affected batch is re-reviewed. Accuracy thresholds are agreed before work begins. Mid-program renegotiation isn’t how this works.
Audio is collected and annotated by native speakers of the target language and dialect — not transcribed in English and translated. Images and video are annotated by contributors with the cultural and linguistic context to label them accurately for the target market. The 155+ locale figure applies across all modalities.
Welo Data handles contributor sourcing, consent, rights clearance, and structured delivery. Compliance requirements — GDPR, HIPAA, and equivalent — are scoped into program design from the start and documented throughout. Not handled on request after the fact.
For programs pairing modalities — image-text, audio-visual, video-caption — semantic consistency across the pair is a discrete QA step before delivery. Individual label accuracy is necessary but not sufficient; the relationship between paired elements is reviewed separately. Mismatches are corrected and re-reviewed before the dataset leaves the pipeline.
Yes. Secure lab setup, safety compliance, roster management, multilingual motion and voice collection, annotation, and structured delivery. See how Welo Data runs physical AI programs →
GET STARTED
If the data layer fails, the model fails.
Tell us what you’re building. We’ll tell you how we’d run it.