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.

Explore how an EdTech company boosted its profitability and expanded its market by moving its AWS infrastructure from Ireland to Germany. This shift complied with strict German data protection laws and improved system performance while cutting operational costs, creating new opportunities in Europe.

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.

EyeAI 👁️ is a SciForce product, transforming your existing cameras into a smart system for optimizing space and managing queues. Get real-time insights into visitor behavior for better space use and personalized service in retail, healthcare, HoReCa, and public safety—no new hardware needed.

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.

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.