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 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.

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 private horse club that provides full-time care and boarding for pedigree horses owned by individuals. The smart farming & animal wellness club handles all daily responsibilities — feeding, cleaning, grooming, exercise, and health checks — as owners rarely visit in person. Some horses are fully owned by clients, while others are co-owned with the club as part of long-term investment agreements. Owners expect high standards of care and regular updates without having to contact staff directly. To meet this need, the club introduced an AI-powered horse care system with 24/7 video access and real-time stall condition tracking (temperature, humidity, etc.), and automatic alerts — helping owners stay informed about their horses at any time.

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.