Data-Driven Project Analysis: Analyzing Trello Kanban Projects with AI on AWS Bedrock

This article presents a practical use case that leverages AWS services and generative AI to enhance project analysis and interpretation of Trello Kanban boards. It discusses how AI can be used to extract insights from task metadata and comments, enabling more objective decision-making.

Details

The article explains how modern software projects often involve multiple distributed teams working on high-complexity initiatives, with frequent releases and ongoing production fixes. While tools like Kanban boards help organize tasks, epics, and workflows, they also generate large volumes of unstructured data in the form of comments, status changes, and timelines. As the number of interdependent tasks and contributors grows, understanding the real state of a project and identifying early risks or bottlenecks becomes increasingly difficult. The article presents a solution that leverages AWS services and generative AI to analyze task metadata and detect semantic patterns in comments, enabling more objective insights, early warnings, and data-driven decision-making. It provides an overview of Kanban and Trello, explaining how user stories can be leveraged as a data structure for AI interpretation. The article also introduces AWS Bedrock and Amazon Nova, which provide a unified platform for building AI-powered applications, and outlines the reference architecture for the proposed solution.