About clientOur client is a multinational healthcare company, operating in multiple countries and serving millions of clients. Recognized as one of the leading brands in the healthcare sector, the company is known for its substantial revenue and global presence.
The client required specialized software to support a laboratory using expensive and complex diagnostic instruments. The challenge was to reduce costs, minimize equipment failures, and ensure the constant availability of tools for uninterrupted work. Additionally, the goal was to improve the diagnostic devices through appropriate maintenance and component optimization.
- Solution Architect
- DevOps 2
- Software Developers
We assembled a dedicated team that developed simple yet robust software for hardware monitoring and workflow management. Utilizing IoT, we enabled continuous monitoring of diagnostic instruments. This integration of IoT with predictive maintenance significantly improved the management of equipment health.
Technology used in project
AWS Serverless architecture, React.js, Graphql, AWS AppSync, AWS Cognito, DynamoDB, Terraform, Gitlab pipelines, Node.js
Value we added
Our collaboration resulted in better equipment utilization, less downtime, and fewer queues. We extended the time between services, optimizing maintenance schedules to be neither too frequent nor too sparse. By collecting historical data on failures, services, and repairs, we reduced the number of malfunctions and downtime. This led to significant savings, ensured equipment availability, and improved overall product quality through data-driven component optimization. The flexible data products developed allowed for broader team collaboration and utilization.
The client is pleased with our cooperation and plans to expand the system to include more devices and business areas, enhancing the laboratory management system. We are exploring ways to develop more engaging features for the software to attract a wider audience, focusing on the integration of Data Mesh-based solutions for building data products aimed at predictive maintenance.