Our client was a U.S.-based, multi-institutional healthcare research initiative developing an AI-ready clinical data ecosystem for critical-care research. The initiative brought together several institutional data sources with differences in clinical workflows, source systems, coding practices, data availability, and OMOP ETL implementations. The client needed a systematic way to translate complex clinical research questions into reproducible computable phenotypes that could support cohort discovery, population characterization, outcome definition, and downstream analytical and machine-learning workflows. The project focused on structured electronic health record data represented primarily through the OMOP Condition, Procedure, Measurement, Observation, Device, Drug, Specimen and Visit domains. To preserve confidentiality, the organization and individual clinical use cases are not disclosed.

Our client is a U.S.-based startup specializing in automated roof measurement for the insurance industry. Their core business involves providing insurers with precise roof dimensions, structural layouts, and damage assessments based on drone imagery. To improve accuracy and reduce manual effort, they needed a custom software solution that could automatically reconstruct roofs in 3D, extract relevant measurements, and generate clean 2D plans suitable for underwriting and claims.

Our client, a U.S. health insurer collaborating with multiple hospital systems, aimed to aggregate and harmonize anonymized claims and clinical data in the PCORnet Common Data Model (CDM) to support large-scale outcomes research and operational analytics. The incoming medical and billing feeds came from heterogeneous hospital and payer systems with inconsistent schemas, variable data quality, and no unified governance. The client asked SciForce to design and implement a sustainable, cloud-native ETL/ELT pipeline on Snowflake that would: 1) Continuously integrate raw source feeds into a centralized Snowflake data platform; 2) Transform them into a PCORnet-conformant CDM with strong data quality guarantees; 3) Enable near real-time analytics for patient demand forecasting, capacity planning, and revenue cycle optimization.

The client is a hardware and infrastructure provider developing a platform for delivering virtual data centers as a scalable, cost-efficient service. The project’s goal was to enable enterprise customers to deploy and manage computing resources — including virtual machines, storage, and network components — through a unified, automated environment. The platform was designed to integrate physical infrastructure with software-defined orchestration, providing secure tenant isolation, flexible resource allocation, and end-to-end automation. By relying on open-source technologies and custom orchestration components, the client aimed to achieve the reliability and manageability of enterprise-grade systems while keeping operational costs under control.