The DATALENT
Data Pipeline

We capture data in the field, in controlled lab environments, and from digital sources depending on the domain. Urban scene data comes from real city cameras. Medical data comes from clinical partners. Nothing is simulated or synthetically generated at this stage.

Raw data is ingested and passes structural validation, near-exact and semantic deduplication, and source verification. This step removes an average of 12–18% of raw data before any human touches it. Only data that passes enters the annotation queue.

Domain experts annotate our data, not crowd-workers. Radiologists annotate medical scans. CV engineers annotate urban scenes. Linguists annotate language data. We use inter-annotator agreement tracking and adjudication protocols. For medical imaging, secondary expert review is mandatory.

Multi-layer QA: automated checks cover label completeness, range violations, and statistical outliers. Expert human review covers annotation accuracy on a statistical sample. Anything below threshold goes back for re-annotation. Published QA pass rate is 99.2%. The 0.8% that fails gets regenerated, not shipped.

Before any data leaves our infrastructure, PII detection runs on all data types including metadata, EXIF, embedded identifiers, and text. Anonymization is applied. Consent records are verified per source. Licensing is cleared. GDPR, HIPAA, and CCPA requirements are validated. A compliance certificate is issued with each batch.

Compliant, validated datasets packaged with standardized schemas, data cards, version metadata, and audit documentation. Delivered via secure API, encrypted S3-compatible transfer, or enterprise connector. HuggingFace Datasets and PyTorch DataLoader compatible out of the box. All datasets versioned for reproducible training.