Case studies

For a video processing platform handling high upload volumes, we developed an automated, scalable pipeline using Python, AWS ECS, and Fargate. This smart solution dynamically scales, reduces resource waste, and enables seamless, hands-free video processing across formats—empowering the client to meet demand peaks effortlessly.

This case study examines the optimization of a major international publishing house's e-commerce infrastructure, which suffered from significant performance issues due to an outdated single-server setup. Our job was to optimize the page load time and system reliability by enhancing the server capacity, and introducing automated deployment processes.

The implementation of AI-driven technologies enabled efficient handling of diverse receipt formats for taxi services. Using advanced OCR with Tesseract, GANs for image enhancement, and LLMs for data structuring, we created a comprehensive solution that ensured high data accuracy and reduced processing times.

Our project advanced by adding specific new functionalities and technological enhancements, all while keeping the system fully operational. We focused on customizing Kubernetes configurations for AWS, streamlining security with role-based access (RBAC), and leveraging CI/CD pipelines for more efficient workflows. This ensured seamless service continuity amid significant infrastructure upgrades.