We support the collection and generation of datasets across multiple modalities including images, videos, text, audio, and multimodal data. Data can be sourced from real-world environments, licensed datasets, or synthetic generation pipelines depending on the project requirements.

We evaluate the model objective, required dataset coverage, and project timeline to determine the optimal sourcing approach. Depending on the use case, this may involve real-world data collection, licensed datasets for faster startup, or synthetic data generation to expand edge cases.

Yes. We work with ML teams to define dataset specifications, including label space, class balance, coverage requirements, and dataset splits (train, validation, test) to ensure the dataset supports both training and evaluation workflows.

We apply automated validation checks together with sampling audits to verify data consistency, metadata completeness, and dataset distribution. These checks help detect issues such as missing metadata, corrupted files, or abnormal data distributions before delivery.

Our data pipelines include deduplication checks and controlled dataset splits. We also validate train/validation/test partitions to prevent data leakage that could negatively affect model evaluation.

Datasets can be delivered in widely used formats depending on the use case, including COCO, YOLO, Pascal VOC for computer vision tasks, JSONL for NLP datasets, and CSV or Parquet for structured data.

Yes. We recommend delivering datasets with accompanying documentation (dataset datasheets) describing dataset sources, composition, generation processes, and known limitations to support transparency and reproducibility.

After defining the dataset scope and requirements, most pilot projects can begin within a few working days. Pilot phases typically focus on validating the data collection strategy and quality metrics before scaling.