HealthVerity provides privacy-protected real-world data exchange on which pharma and life sciences companies license insurance, pharmacy, lab and hospital data.
Product Overview on DataChain, shown as an example.
DataChain lets Python users turn files in S3, GCS, and Azure into versioned, typed datasets that are queryable at warehouse speed, using the Compute Engine for heavy file-level processing. The Compute Engine runs heavy Python work on object-storage files, including large language model calls, model inference, multimodal extraction, and per-row input and output. It runs in parallel by default, handles network round-trips asynchronously, distributes work across machines through Studio, and can recover from checkpoints.
The Dataset DB holds typed, versioned datasets deposited by chains and serves filter, join, group_by, and similarity-search operations over them at warehouse speed. The transpiler dispatches per-row Python to the Compute Engine and per-column filter, join, group_by, and similarity expressions to the Dataset DB; Pydantic types flow through both without separate schema registration.
