Service

Clinical Data Pipelines

Config-driven ETL for imaging and clinical data — ingestion, de-identification, validation, and delivery you can actually audit, built for health systems nationwide.

Pipelines that survive contact with production

A lot of clinical data pipelines are one-off scripts held together by whoever wrote them. That works until that person leaves, or a new data source shows up, or an auditor asks for a record of exactly what happened to a given study. We build pipelines that are config-driven, observable, and safe to hand off.

What we do

Ingestion — pulling imaging and clinical data from PACS, VNAs, and modality feeds into a structured, versioned pipeline.
De-identification & validation — built-in checks so bad or incomplete data gets caught before it reaches a downstream research or AI system.
Delivery — structured, auditable handoff to research platforms, AI training environments, or downstream analytics — with a record of what moved and when.
Python-first tooling — pipelines written to be read and maintained by your own team, not a black box.

Every pipeline we build produces an audit trail by default — not bolted on after a compliance review flags the gap.

Who this is for

Health systems building research data infrastructure, imaging AI teams sourcing training data at scale, and any organization that needs to prove — not just claim — what happened to clinical data along the way.