Amazon Bedrock is being used to automate email management for public sector organizations, helping them sort and prioritize incoming messages more efficiently. The technology enables intelligent routing of emails based on urgency and departmental relevance, allowing staff to focus on high-value tasks. This solution is particularly beneficial for local governments, where councillors receive a wide range of communications across multiple service areas. By using generative AI, the system can classify and direct messages to the appropriate departments, such as IT, Children’s Services, and Housing, ensuring urgent matters receive immediate attention. The system also supports faster response times and better service delivery for constituents. According to the source, the solution uses Amazon Bedrock and other AWS services to automatically categorize, augment, and prioritize incoming email messages to the appropriate departments while assessing their urgency. It reduces the manual workload for staff and provides a starting point for further development. The architecture involves uploading emails to an Amazon S3 bucket, which triggers an Amazon EventBridge rule to send messages to an Amazon SQS FIFO queue. This queue is then connected to an AWS Step Functions state machine that invokes an Amazon Bedrock model to analyze and classify the email content. The response from the model is saved into an Amazon S3 bucket, where it is processed by an AWS Glue crawler to create metadata for a data catalog. Councillors can access comprehensive email analytics through a dashboard built using Amazon Quick Sight, which provides insights on categorization, severity, and urgency. The analytics allow each councillor to ask questions about the data using the Q&A capability in Amazon Quick Sight. The system also requires specific AWS services and permissions for implementation, including Amazon S3, Amazon Bedrock, AWS Step Functions, and Amazon Quick Sight. The solution is designed to help public sector organizations optimize their resources and improve constituent service.
Source: awsml
