Case studies

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 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 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.

Large data volumes are revolutionizing industries, including AI in medical research. This influx of data enables observational studies that harness global statistical evidence for real-world evidence generation. However, conducting such studies can be labor-intensive and prone to inconsistencies in health data standardization due to disconnected communication channels, like repositories, emails, forums, and chats. Moreover, adapting code to different environments during the execution phase can create unscalable and non-reusable analytical frameworks, while the lack of a secure data exchange platform further complicates collaboration. In response, the OHDSI community is developing ARACHNE, a DevOps-enabled research analytics platform for life sciences and an innovative real-world data (RWD) platform for automated orchestration of observational healthcare research across distributed research networks in life sciences, healthcare, academia, and organizations handling patient-level data

For an AI-powered automated video processing pipeline handling high upload volumes, we developed a serverless video processing solution with AWS Fargate and ECS, using Python to orchestrate event-driven workflows. This smart solution AI-powered media workflows, reducing resource waste, and enabling automated media workflows for hands-free video processing across formats—empowering the client to meet demand peaks effortlessly. The client operates a video processing platform that specializes in converting, compressing, and optimizing video content using machine learning for video optimization for media companies and individual creators. Their platform supports multiple formats enabling users to prepare videos for seamless online distribution across various devices and platforms. The platform is designed to handle large volumes of video content, ensuring high-quality processing while meeting the