Case studies

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.

We developed an AI speech recognition solution for disabilities that converts speech to text and enables speech-to-speech transformation, specifically designed for individuals with speech impairments. The goal is to enhance smart assistant functionality through assistive speech technology, allowing users with mobility and speech challenges to customize commands, train the system to recognize their unique speech patterns.

The client works in the financial sector, helping businesses simplify reporting, budgeting, and forecasting. Their tools use real-time data and smart technology to create accurate reports, plan for the future, and test different scenarios. With features like multi-company and multi-currency support, easy-to-use dashboards, and seamless ERP integration, their solutions are flexible and scalable, making them ideal for businesses of all sizes looking to improve financial processes and support growth.

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.