Nightingale provides a managed, full‑stack clinical data infrastructure that is embedded directly into each trial. It designs, builds, and operates real‑time data pipelines—ingesting data from devices, labs, and ePROs, normalizing it to regulatory standards, and automating reconciliation—to eliminate manual cleanup, reduce operational overhead by up to 70%, and accelerate database lock timelines.
Funding
Funding not disclosed
Founders
Product
Problem
Clinical trial data infrastructure is fragmented, requiring custom pipelines for each study and leading to schema drift, extensive manual reconciliation, and delayed database locks.
Solution
Nightingale offers a full‑stack, managed data infrastructure that is embedded into each trial. The company assesses the study environment, designs and builds a real‑time data pipeline, and operates it for the trial’s duration. By ingesting high‑frequency data from devices, labs, and ePROs, normalizing it to study‑specific and regulatory standards, and automating reconciliation, Nightingale eliminates manual cleanup. Real‑time data delivery and integrated dashboards enable faster decision‑making and shorten database lock timelines. Ongoing monitoring and optimization ensure reliable performance from the first patient enrollment through study completion.
Target Audience
Primary customers are contract research organizations (CROs) and academic medical centers that run clinical trials and need reliable, low‑overhead data management.
Features
- End‑to‑end pipeline construction that ingests data from devices, sensors, labs, and electronic patient‑reported outcomes
- Automatic schema management and harmonization to study‑specific and regulatory standards, preventing drift
- Automated reconciliation workflows that replace manual data cleanup, reducing operational effort by up to 70%
- Real‑time data availability with dashboards and operational reporting for immediate insight
- Integration hooks for CTMS, safety databases, and submission pipelines to streamline downstream processes
- Continuous monitoring, maintenance, and performance optimization throughout the study lifecycle