A human data operations partner for enterprise AI.
Argos Data designs collection programs around clear data specifications, contributor criteria, consent standards, secure workflows, and quality controls. Most programs run inside Argos Myriad, where its customizable tooling enables embedded QA controls, scalable workforce deployment, and secure integration with client systems. When clients prefer to operate inside their own platforms or use offline file exchanges, Argos Data adapts to the deployment model the program requires.
Where AI Data Collection is applied.
Six ways we collect.
Each program is built around the model objective, target users, operating conditions, and performance requirements.
Purpose-built multimodal datasets aligned to specific AI model objectives
Language-specific datasets aligned to model objectives, domains, and real-world use cases
Targeted sourcing for languages, dialects, and regional variants underrepresented in mainstream AI training data
Voice and audio datasets that reflect real-world speakers, environments, and use cases
Secure, model-ready datasets across text, image, audio, and video for multimodal AI systems
Human-validated synthetic datasets for edge cases, rare scenarios, and controlled model coverage
Collection, treated as an engineered data operation.
AI systems are only as reliable as the data they are built on. Generic, incomplete, or poorly matched datasets limit model accuracy, introduce bias, weaken multilingual performance, and create downstream rework across annotation, fine-tuning, and evaluation.
Argos Data treats collection as an engineered AI data operation. We define target data requirements, contributor profiles, domain criteria, locale needs, validation rules, and QA checkpoints before collection begins, drawing on three decades of multilingual experience and a vetted global network of 80K+ contributors. Programs are designed around the model's intended use, target users, and operating conditions rather than treating collection as a volume exercise.
For enterprise AI teams, this turns data collection into a controlled function of model development. The result is cleaner inputs, stronger relevance to production conditions, and a more reliable foundation for scalable AI programs.
Representative, high-quality datasets aligned to model goals and production use cases.
AI Data Collection gives enterprise AI teams representative, high-quality datasets aligned to model goals and production use cases. The result is cleaner input data, stronger model relevance, improved multilingual and multimodal performance, and a more reliable foundation for scalable AI development.