Root cause analysis (RCA) entails identifying the primary issue when a customer problem arises, addressing their concerns, and suggesting preventive measures to avert similar future issues. While crucial for improving customer satisfaction, the RCA process can become repetitive and benefit from automation. Our focus lies in developing an analyzer to assist the customer support team in delivering efficient service by automating tasks and reducing time and effort. This analyzer follows a standardized process akin to manual procedures: Gathering customer domain details, analyzing logs for errors, consolidating results, and generating comprehensive reports detailing potential root causes and preventive measures. Some issues are already resolved using this analyzer. Our approach leverages various Amazon services. Amazon Web Service (AWS) Support Ticketing facilitates customers in creating and tracking support cases, providing regular updates to expedite issue resolution and optimize AWS product usage. Amazon Visual Private Cloud (VPC) offers a flexible, secure, and scalable solution for managing private networks within AWS, ensuring resource isolation and network customization. AWS Step Functions empower users to construct and manage intricate workflows, monitoring each step’s status to ensure proper application functionality. Additionally, AWS Lambda plays a critical role in building serverless applications, defining event responses and data processing, including log management for identifying potential root causes. The implementation of these analyzers has significantly reduced customer query response time, enhanced satisfaction levels while also reducing system downtime and manual troubleshooting expenses. Authors continue to improve the analyzer by introducing features to solve different type of issues, boosting its efficiency and expanding its utility.