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Praedico, a global railway technology company with teams in the Netherlands, Australia, and the USA, specializes in applying artificial intelligence to optimize rail network reliability, efficiency, and safety. Their mission is to use predictive insights to reduce downtime, improve asset performance, and make rail operations more resilient.
Over the past two years, we have partnered with Praedico to co-engineer an AI-powered platform for predictive rail maintenance. By analyzing real-time sensor data from trains and infrastructure, the system predicts component failures before they occur. This enables condition-based maintenance, reduces operational disruptions, and supports safer, more cost-effective rail networks.
Our remote engineering and QA specialists became an extension of Praedico’s team, delivering expertise in software engineering, front-end performance, and testing. Together, we built a system designed for both technical reliability and seamless user experience.
Praedico’s vision demanded more than a strong AI core. The platform needed to deliver real-time insights in a way that operators could trust and act upon quickly. This meant upgrading front-end performance for responsiveness, embedding rigorous QA practices to ensure reliability, and standardizing development workflows for efficiency as the platform scaled.
At the same time, the collaboration had to bridge geographies. With teams spread across Europe, Australia, and Asia, success relied on agile practices, transparent communication, and a delivery model that could harness talent globally while staying cost-efficient.
We enhanced the platform’s front-end architecture, transitioning from JavaScript to TypeScript and leveraging React, ArcGIS, and Chart.js to deliver smoother, faster interfaces. This upgrade made data visualizations more intuitive for operators, ensuring that predictive insights could be understood and acted upon without delay.
To ensure quality at scale, we established a comprehensive QA framework from the ground up. Manual, automated, and regression testing were combined with UI automation via Playwright and backend automation using C# with xUnit. By embedding shift-left practices and integrating QA into CI/CD pipelines with GitHub Actions, we caught defects earlier, accelerated release cycles, and improved cost efficiency.
We also standardized development practices with ESM modules and React Query, streamlining collaboration across distributed teams. This created consistency, improved maintainability, and reduced onboarding time for new engineers.
Through agile delivery, our Sri Lanka-based engineering and QA specialists worked in close sync with Praedico’s teams in the Netherlands and Australia. Despite time zone differences, continuous communication and sprint-based collaboration ensured alignment and timely delivery.
We enhanced the platform’s front-end architecture, transitioning from JavaScript to TypeScript and leveraging React, ArcGIS, and Chart.js to deliver smoother, faster interfaces. This upgrade made data visualizations more intuitive for operators, ensuring that predictive insights could be understood and acted upon without delay.
To ensure quality at scale, we established a comprehensive QA framework from the ground up. Manual, automated, and regression testing were combined with UI automation via Playwright and backend automation using C# with xUnit. By embedding shift-left practices and integrating QA into CI/CD pipelines with GitHub Actions, we caught defects earlier, accelerated release cycles, and improved cost efficiency.
We also standardized development practices with ESM modules and React Query, streamlining collaboration across distributed teams. This created consistency, improved maintainability, and reduced onboarding time for new engineers.
Through agile delivery, our Sri Lanka-based engineering and QA specialists worked in close sync with Praedico’s teams in the Netherlands and Australia. Despite time zone differences, continuous communication and sprint-based collaboration ensured alignment and timely delivery.
The partnership with Praedico has elevated the quality, reliability, and scalability of their predictive rail maintenance platform. By reducing platform bugs and improving release speed by 40 percent, we significantly enhanced product quality. Faster, more intuitive user interfaces have improved adoption among operators and stakeholders, making predictive maintenance insights more actionable.
The adoption of cloud-native, scalable engineering practices has laid a strong foundation for rapid growth, supporting future integrations with diverse rail sensors and data sources. At the same time, the remote delivery model reduced development and HR overheads, proving that global collaboration can deliver both innovation and cost efficiency.
Most importantly, this partnership has strengthened Praedico’s position as a pioneer in predictive rail technology. Together, we have shown how AI, rigorous engineering, and remote collaboration can transform rail maintenance into a smarter, safer, and more sustainable practice for the future of transportation.