Everything teams ask before partnering with DATALENT. If your question is not here, ask us directly.
What makes DATALENT different from other AI training data providers?
Most data providers either use synthetic generation or crowd-sourced annotation. DATALENT does neither. Every dataset is captured from real-world environments and labeled by domain experts — radiologists for medical data, computer vision engineers for sensor data, native-speaker linguists for language data. This is the reason our clients consistently see larger accuracy gains than they achieve with synthetic or crowd-sourced alternatives. We also build compliance into every stage of the pipeline rather than adding it as an afterthought.
How do you ensure HIPAA and GDPR compliance for sensitive datasets?
Compliance is built into our pipeline before data reaches the annotation stage. For medical data, we apply de-identification that exceeds HIPAA Safe Harbor — removing all 18 identifiers plus running secondary risk assessments. For all datasets, we run PII detection across text, metadata, EXIF data, and embedded identifiers. Every dataset ships with a compliance certificate, full data lineage documentation, and audit records. Our clinical partners all operate under executed Business Associate Agreements (BAAs).
What format do datasets ship in, and are they compatible with my training framework?
All DATALENT datasets are HuggingFace Datasets compatible out of the box, which means they work with PyTorch DataLoader, TensorFlow tf.data, and any framework that supports the HuggingFace ecosystem. We also provide COCO JSON for vision datasets, YOLO format, and CSV/Parquet for tabular data. Every dataset ships with a standardized schema, a data card documenting distribution and known limitations, and version metadata for reproducible training. Custom schemas are available for enterprise clients.
How long does it take to receive a dataset after requesting it?
For catalog datasets — those already processed and available — delivery averages 48 hours from purchase approval. This includes compliance verification, packaging, and secure transfer setup. For custom datasets, timelines depend on scope: most projects complete in 3 to 6 weeks from scoping call to delivery, with milestone sample reviews along the way. Enterprise clients with dedicated pipelines receive updates on a continuous delivery schedule agreed at onboarding.
Can you build a completely custom dataset for our specific use case?
Yes — and this is a significant part of what we do. Custom projects start with a scoping call where we understand your model architecture, existing data situation, and what a successful dataset looks like for your team. From there we produce a detailed data brief covering sources, annotation schema, scale, timeline, and compliance plan. You review and sign off before we start. Most custom projects include weekly milestone deliveries with sample reviews at each stage.
Learn more about custom datasets.
Do you offer a trial or sample before we commit to a full dataset purchase?
Yes. For any catalog dataset, we provide a representative sample — typically 500 to 2,000 examples — before your team commits. For the API, there is a free tier that allows access to 10,000 samples across three domains with no time limit. For custom projects, the scoping process includes sample annotation reviews at milestone stages. We understand that data quality decisions are high stakes — we want you to be confident before committing.
