Where GenAI meets
the real world.

Curated Experts

Global Regions

Secure Facilities

Welocalize ISO Certifications

workday squarespace google stopify dropbox

Where Teaching Models to Speak Human  Matters Most

Autonomous
Vehicles

Foundation
Model Alignment

RECOGNITION

2026

WINNER

2026

WINNER

Global Business Tech Awards 2026 Finalist

2026

FINALIST

A.I. Awards winner image

2025

SHORTLISTED

The full stack of human intelligence for AI development.

From scope to production-ready
data and evaluation.


We align on use case, languages, domains, quality thresholds, and deliverable format with your team before a single task is assigned.


Domain experts and native speakers are selected from our 500K+ vetted workforce. Every contributor is matched to the task, not randomly assigned.


Multi-layer QA with NIMO monitoring every session in real time. Inter-annotator agreement tracked continuously. Quality scores above 90% maintained throughout.


Structured data in your preferred format. Accuracy improves +10% per iteration. Ongoing support as your model evolves, with full auditability at every stage.


Every AI use case.
One trusted partner.

Text & NLP

Language data grounded in how people actually write and speak.

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Instruction tuning & SFT data
High-quality prompt-response pairs written by domain experts, not scraped or synthetically generated without human validation.
Named entity recognition & classification
Precise labeling across legal, medical, financial, and technical domains. Consistency enforced by NIMO across every annotation session.
Summarization & generation evaluation
Human judges assess relevance, factuality, coherence, and tone, structured rubrics designed around your model’s specific risk profile.
Multilingual NLP across 155+ locales
Not just translation, cultural grounding, dialect coverage, and native fluency. Gaps that automated approaches cannot close.
Audio & Voice AI

Voice data that reflects how real people talk, not how scripts were read.

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Speech transcription & diarization
Native-speaker transcribers across 100+ languages, including low-resource dialects rarely covered by automated ASR systems.
Audio data collection for Voice AI
Scripted and spontaneous recordings from diverse speaker populations. Controlled acoustic variation for robust ASR and Voice AI training.
Emotion & sentiment labeling
Affective annotation by trained raters who understand cultural norms around emotional expression, not crowdsource approximation.
TTS & voice model evaluation
Expert phoneticians and language specialists assess TTS output and speech model performance against native-speaker standards across 155+ locales.
Vision & Multimodal

Image, video, and cross-modal annotation for robotics, AV, and beyond.

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Object detection & segmentation
Precise bounding boxes, polygons, and semantic masks, validated by multi-annotator consensus and NIMO quality monitoring.
AV & robotics perception data
LIDAR point cloud annotation, sensor fusion labeling, and spatial scene understanding for autonomous vehicle and robotics programs.
Video temporal annotation
Frame-level and clip-level annotation for action recognition, activity detection, and video understanding at scale.
Image-text alignment evaluation
Human judges assess whether captions and model descriptions accurately reflect image content, critical for multimodal model evaluation.
RLHF & Alignment

Preference data that reflects real human values, not averaged crowd opinion.

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Pairwise preference ranking
Side-by-side comparison tasks designed to elicit genuine preference, not anchoring bias or positional effects that contaminate crowdsource RLHF.
Constitutional AI evaluation
Structured rubrics for harmlessness, honesty, and helpfulness, applied by trained raters who understand the distinction, not checkbox workers.
Safety & red-teaming
Adversarial probing to surface failure modes before deployment. Documented methodology that satisfies enterprise AI governance requirements.
Iterative fine-tuning support
Continuous evaluation loops that improve model accuracy +10% per iteration. Welo Data supports the full RLHF cycle, not just the first pass.
Agentic & Reasoning

Evaluation infrastructure for agentic AI, where automated metrics fall short.

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Multi-step task evaluation
Human judges assess agentic task completion, not just final output, but the quality of intermediate reasoning steps that automated metrics miss.
Tool use & function call validation
Expert evaluators assess whether agents select and use tools correctly, across domains from code execution to web browsing to API calls.
Chain-of-thought & reasoning traces
Structured evaluation of reasoning quality, logical coherence, step validity, and alignment between stated reasoning and final outputs.
Custom benchmark design
Evaluation frameworks built for your model’s specific domain and deployment context, not adapted from generic public benchmarks.

Proven in production.

Scaling QA across three global regions without losing fidelity

Building reliable coding benchmarks for data science agents


Multilingual precision at scale: machine translation post-editing


“The quality bar Welo Data holds their contributors to is genuinely different. We’ve worked with other annotation vendors. The difference isn’t marginal, it’s the reason our model performs the way it does in production.”

What model builders and enterprises ask us.

Tell us your use case, languages, and quality requirements, our team will come back with a clear picture of scope, timeline, and what delivery looks like.

No. Welo Data is a managed services partner, not a self-serve marketplace. Every program is scoped, staffed with domain-matched contributors, and monitored by our team using NIMO, our proprietary quality system. You work with a dedicated program team, not a platform dashboard.

Timeline depends on task complexity, language coverage, and domain specificity, all of which we assess in the scoping conversation. Our team moves quickly once requirements are clear, and contributor matching typically happens in parallel with finalizing task design.

NIMO monitors 130+ behavioral variables across every annotation session, not just final outputs. Inter-annotator agreement is tracked continuously. Fraudulent contributors are blocked before they touch your data. Quality scores are consistently above 90%, with accuracy improving +10% per iteration.

7 ISO certifications, SOC 2, GDPR. 14+ secure facilities globally. Full audit trails on contributor identity, task assignment, and quality monitoring, so your governance team can answer how your training data was produced, by whom, and under what controls.

Yes. 155+ locales including dialects and regional variants that most annotation vendors simply don’t cover. Our 25+ years of language services work means we have established contributor networks in markets where others have to start from scratch.

Three things competitors can’t replicate: NIMO (our proprietary quality monitoring system, not a checklist layer); 25+ years of language services DNA from our Welocalize parent (actual multilingual infrastructure, not a translation API); and a rigorous contributor qualification process that produces domain-matched specialists, not a generic crowd.