Case studies

Our client in the corporate EdTech sector developed one of the AI-powered personalized learning platforms for corporate training that aggregates business courses for easy access and management. They sought an automated system to categorize courses into areas like marketing, finance, and HR, and to recommend personalized learning paths based on each employee's role and career goals, enabling a data-driven learning transformation across the organization. Read on to learn about our ML solution that transforms course categorization and tailors personalized recommendations.

Jackalope automates transforming complex medical data into standardized formats of OMOP CDM and SNOMED Clinical Terms (CT). This enables healthcare providers, practitioners, and scientists to effectively use real-world evidence (RWE) in healthcare for research and delivering enhanced patient care.

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, 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 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.