Data for ML & AI
High-quality web training datasets and large-scale labeling services designed for machine learning models, NLP systems, and AI applications requiring internet data.
Power Your Models with Quality Web Data
Training Datasets
Pre-collected and custom web datasets for training classification, NLP, image recognition, and other ML models. Structured, clean, and ready for model ingestion.
Learn MoreLarge-Scale Labeling
Human-validated labeling services for websites, images, and content at scale. Custom taxonomy development and quality assurance for training data.
Learn MoreML-Ready Web Data
Machine learning models are only as good as their training data. WebRefer provides web datasets specifically curated and prepared for ML applications—clean, structured, labeled, and documented to accelerate model development and improve performance.
Our data sources and methodology enable collection and processing at scales that would be impractical for individual teams to develop. From millions of labeled website screenshots to comprehensive text corpora, we deliver the training data volume and quality that modern ML requires.
Data Types
We provide website screenshots at scale for image classification and computer vision. Structured HTML and DOM data for content extraction models. Text corpora from web content for NLP applications. Technology classification labels for website categorization. Company and organizational attributes for entity recognition. Custom data collection for specialized model requirements.
Quality Assurance
ML training data requires rigorous quality control. Our labeling processes include multiple validation passes, consistency checking, and accuracy verification. We provide detailed documentation and data dictionaries to ensure proper model integration. Our accuracy standards match the requirements of production ML systems.
Applications
Research teams developing new ML approaches access comprehensive web datasets. Product teams training classification models for web content, companies, or images. NLP applications requiring large text corpora with structural annotations. Computer vision projects needing labeled website screenshots at scale.