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 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 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.

The client was a public-sector healthcare organization focused on automated epidemiological monitoring and preparedness. They needed an automated disease spread forecasting system to predict the spread of illness across administrative districts based on hospital-reported case data. Key priorities included low-maintenance deployment, seamless integration with existing health data pipelines, and the ability to scale across geographic units. Their use case demanded robust AI in healthcare infrastructure to ensure consistent model retraining, evaluation, and deployment, minimizing manual oversight while maintaining high model performance.

The client provides a construction intelligence platform that aggregates raw construction data from a wide range of regional providers. This includes details about active and planned building projects, such as contractors, costs, timelines, and locations. The data is often incomplete or inconsistent across sources, requiring significant processing before it becomes usable. To address this, the client required an AI pipeline for construction and infrastructure data aggregation, combined with robust data engineering for PropTech to support future analytics and automation. The goal was to build a system that could handle large volumes of messy, semi-structured data, align it to a unified schema, and enrich it with geographic and financial context. The processed data would then serve as the foundation for AI for infrastructure analytics, machine learning, and client-facing reporting, showing how to build filterable construction intelligence from raw data feeds.

The client is a pharmacy chain looking to deploy a computer vision system for queue detection in retail stores to optimize customer flow and improve service efficiency. The system should support queue management using AI, detecting when queues start forming and automatically alerting staff when help is needed at the counter. They also want to tell the difference between quick online order pickups and longer in-person consultations, to better understand service times. The AI for retail efficiency system should work with their existing CRM to identify order types. Most of their stores are small, so one or two cameras are enough. The solution should be lightweight and run on their current infrastructure.