Organizations process millions of documents daily, from insurance claims and invoices to legal contracts and medical records. While traditional optical character recognition (OCR) solutions extract text, they can’t understand context, relationships, or meaning embedded within complex documents. This limitation creates bottlenecks that require manual intervention, increasing processing time and costs while introducing potential errors.

Amazon Bedrock Data Automation (BDA), provides a unified API experience for extracting meaningful insights from multimodal content, including documents, images, videos, and audio files. Unlike traditional solutions that focus on text extraction, BDA understands document context, validates extracted data, and provides confidence scores for accuracy. BDA processes documents through a pipeline that automates complex tasks including document classification, extraction, normalization, and validation. When a document is submitted, BDA automatically splits it along logical boundaries, classifies each section into appropriate document types, and matches them to the correct processing blueprints. This intelligent routing removes the need for manual document sorting and orchestration of multiple AI models. The service supports a wide range of file formats, with support for up to 3,000 pages and 500 MB per API request, making it suitable for processing diverse document types at scale.

This post outlines the development of a cost-effective and scalable intelligent document processing pipeline on AWS, powered by Amazon Bedrock and its features. BDA is a managed service within Amazon Bedrock that automates the extraction of insights from documents. We demonstrate how BDA extracts and analyzes document content, while Strands Agent hosted on Amazon Bedrock AgentCore Runtime coordinate specialized processing tasks, and Amazon Bedrock Knowledge Base enable contextual understanding across multiple documents. By combining these capabilities within a unified architecture, organizations can transform their document processing workflows with minimal development effort.

Our intelligent document processing pipeline combines generative AI with orchestrated workflows to automatically extract, analyze visual plots, graphs, and charts, and derive insights from documents while maintaining context and relationships across multiple data sources.The solution processes documents through four integrated layers:

The input processing layer forms the foundation of this solution. This layer manages the initial reception and routing of incoming documents. A Document Upload Triggers processing workflows when documents arrive in designated Amazon Simple Storage Service (Amzon S3) buckets, supporting various formats including PDFs, and scanned documents (in PDF).

BDA serves as the core extraction engine in the input processing layer, handling document splitting, classification, and content extraction through a unified API. AWS Step Functions orchestrates the workflow to maximize the capabilities of BDA in the Extraction and Storage Layer, providing operational visibility and control throughout the process. Here’s the detailed orchestration flow: