Public sector agencies process large volumes of unstructured evidence, such as body camera footage, surveillance video, and scanned documents, that require extracting insights before anyone can act on them. This post shows how to combine Amazon Bedrock Data Automation with the Model Context Protocol (MCP) to turn unstructured data into structured insights. You can then expose those insights through natural language queries in an AI agent, such as Salesforce Agentforce.

In our previous post, Modernizing evidence management in Salesforce Public Sector Solutions with Amazon S3, we used the External Storage of Files with Amazon Simple Storage Service (Amazon S3) integration from Agentforce Public Sector (formerly Public Sector Solutions) as an example implementation. With that foundation in place, you now have durable, cost-efficient storage for body camera footage, surveillance video, photographs, audio recordings, and scanned documents.

However, storage is only half the challenge. Without automation, you spend significant time manually reviewing, classifying, and extracting relevant details from these files before you can act on them. With this integration, Agentforce users can search for processed data stored on AWS, surface key insights from unstructured data, and perform more advanced actions, all without leaving the Salesforce console.

Two main flows work together to turn raw evidence into actionable investigative insights. The first flow moves unstructured media files and documents into Amazon S3 using the External Storage of Files with Amazon S3 for Public Sector connector. Figure 1 illustrates how Amazon S3 provides enterprise-scale storage infrastructure for storing large documents and media files.

Source link