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TensorIoT's generative AI solution transformed CircleCI’s error-handling process, making it more intelligent and actionable. The project accelerated their speed to delivery and improved user experience.
How can generative AI be utilized to provide meaningful error responses and streamline the troubleshooting process?
CircleCI now benefits from a more intelligent error-handling system that not only categorizes errors but also sets the groundwork for future AI-driven troubleshooting. This has led to quicker issue resolution and operational efficiency, with plans for further AI integration already in motion.
CircleCI is a leading provider of continuous integration and continuous delivery (CI/CD) services, with a platform that is integral to over 30,000 organizations' software development processes. While their platform excels in automating build tests for code pipelines, it faced a limitation: the error logs generated during test failures were often generic and lacked actionable insights. This presented an opportunity to significantly improve both the user experience and operational efficiency.
The customer's platform excels at running build tests, but there was room for improvement in making the error logs more insightful. These logs, while functional, could be enhanced to better contribute to the speed of delivery, a key success metric for the customer. The goal was to refine the error categorization and reporting system, putting a friendlier face on error handling, without compromising on speed or user experience.
TensorIoT developed a robust solution using generative AI to intelligently categorize error logs. The system identifies the nature of the code failure and categorizes it, paving the way for future enhancements like AI-driven troubleshooting recommendations. The solution was designed for seamless integration into the customer's existing AWS infrastructure, aligning with their plans for scalability and performance