Programmatically creating an IDP solution with Amazon Bedrock Data Automation

2025-12-24 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Data Science & Analytics · Depth: Intermediate, medium

Summary

This article details a programmatic solution for Intelligent Document Processing (IDP) using a combination of AWS services and an open-source SDK. The solution leverages Strands SDK, Amazon Bedrock AgentCore, Amazon Bedrock Knowledge Base, and Bedrock Data Automation (BDA) to automatically extract information from unstructured, multi-modal documents like invoices and reports. It is presented via a Jupyter notebook, demonstrating how BDA acts as a parser for Retrieval-Augmented Generation (RAG) workflows, retrieving relevant context to augment foundational model prompts. A specific use case involves extracting insights from a Nation's Report Card for public school districts from the U.S. Department of Education. The architecture integrates Amazon S3 for storage, Amazon OpenSearch Service for vector embeddings, and employs security guardrails such as IAM role-based access control.

Key takeaway

For AI Engineers building intelligent document processing solutions, this approach offers a robust framework for handling multi-modal content. You should explore integrating Strands SDK with Amazon Bedrock AgentCore and BDA to create scalable RAG workflows, especially for complex data formats. Consider the provided Jupyter notebook as a starting point to deploy and customize your own enterprise-grade IDP applications.

Key insights

Combine AWS Bedrock services with Strands SDK to build multi-modal IDP and RAG solutions.

Principles

• Use BDA as a parser for RAG workflows.
• Strands SDK enables model-driven AI agent development.

Method

Upload documents to S3, create a Bedrock Knowledge Base with BDA parsing, store embeddings in OpenSearch, and deploy a Strands Agent on Bedrock AgentCore for RAG-based question answering.

In practice

• Automate document processing workflows.
• Analyze large-scale multi-modal datasets.
• Build question-answering systems.

Topics

• Intelligent Document Processing
• Retrieval-Augmented Generation
• Amazon Bedrock
• Strands SDK
• Multi-modal AI

Code references

• aws-samples/sample-for-amazon-bda-agents
• aws-solutions-library-samples/guidance-for-multimodal-data-processing-using-amazon-bedrock-data-automation
• awslabs/amazon-bedrock-agentcore-samples
• strands-agents/samples

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.