Case studies

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.

The client is a U.S.–based company developing a computer-vision platform for sports medicine. Its goal is to help professional teams and medical staff prevent injuries by analyzing basketball footage, detecting abnormal movements, and flagging potential risks for review. The project required building a DevOps infrastructure that would let the client’s product run reliably in the cloud and evolve without deployment bottlenecks. This meant designing a secure AWS infrastructure with isolated environments for development and production, automating delivery of containerized applications through CI/CD pipelines, and managing all resources as code for consistency and repeatability. By focusing on cloud-native services, scalability, and automation, the platform established an AWS DevOps setup for scalable AI or computer vision platforms that could grow and adapt reliably.

The client is a DevOps services company developing that helps organizations how to manage Azure AWS and GCP under one DevOps workflow. Many of users operate in mixed environments, which creates challenges in setup, access control, and ongoing maintenance. The platform solves this by allowing engineers to describe infrastructure in simple code files, which are automatically translated into provider-specific resources. In addition to provisioning, it enforces unified security policies, integrates monitoring, and uses reliability controls to keep environments stable. This makes onboarding faster, reduces misconfiguration risks, and provides one consistent way to manage infrastructure across multiple providers.

The client was a residential care facility for elderly patients who were at risk of falling, relying on wearable sensor fall detection for assisted living facilities to reduce incidents and response time. Each resident wore a small motion-tracking device as part of a wearable health monitoring setup that measured their movements in three directions. Their rooms were also equipped with AI in assisted living facilities that monitored temperature, humidity, light, noise levels, and whether someone was in or out of bed. The goal was to build an AI fall detection system that could automatically detect when someone had fallen and notify staff quickly. The team focused on understanding how movement patterns could indicate a fall and explored how room data could help add context in the future.

Our client is a university hospital based in Germany, aimed to enhance cross-institutional standardized healthcare data exchange through structured health data pipelines. Multiple institutions involved in observational research sought integrating observational research data into clinical systems via FHIR, including risk models and disease prevalence metrics, into operational clinical workflows. To support this goal, the client required a conversion pipeline from OMOP CDM (used in research analytics) to HL7 FHIR (used in clinical applications) to enable real-time data AI for healthcare data interoperability.

The client is a mid-sized restaurant chain with about 1200 locations in over 30 countries. Each restaurant provides full-service dining, where waiters take orders, serve food and manage table turnover. Most locations have two dining areas, which can make it hard for staff to keep track of all tables. The client wanted to improve AI for restaurant management by reducing wait times, speeding up table cleaning, and helping staff respond more quickly. Their goal was to use real-time table monitoring and smart video analytics to support better decision-making and improve the overall customer experience, showing how to track table status and wait times using AI in restaurants.

The client is a professional healthcare technology provider whose platform is used by multiple medical institutions to support clinical medical data interoperability. The project focused on enabling medical semantic search using LLMs and vector databases, allowing healthcare teams to structured clinical data extraction from free-text input. To achieve this, the platform integrates large language models for real-time query normalization and a locally deployed Quadrant vector database for high-performance concept retrieval. The solution was designed to deliver accurate concept mapping at scale, while aligning with MLOps and DevOps best practices to ensure reproducibility, modularity, and operational stability across environments.