Unlocking Intelligent Document Processing with Amazon Bedrock Data Automation
Introduction
Extracting information from unstructured documents at scale is a recurring business task…
Solution Overview
The IDP solution presented in this post is deployed as IaC using the AWS Cloud Development Kit (AWS CDK)…
Prerequisites
You can deploy the IDP solution from your local computer or from an Amazon SageMaker notebook instance…
Deploy the Solution
To deploy the solution to your AWS account, complete the following steps…
Using the Solution
This section guides you through two examples to showcase the IDP capabilities…
Pricing
In this section, we calculate cost estimates for performing IDP on AWS with our solution…
Clean Up
To remove the deployed resources, complete the following steps…
Conclusion
Extracting information from unstructured documents at scale is a recurring business task…
About the Authors
Meet the team of experts behind this innovative solution…
Unlocking Intelligent Document Processing with Amazon Bedrock Data Automation
In the ever-evolving landscape of business, extracting information from unstructured documents at scale has become a task of utmost importance. Organizations routinely face challenges like creating product feature tables, extracting key metadata, and analyzing legal contracts, customer reviews, news articles, and more. Traditional methods like Named Entity Recognition (NER) have paved the way but come with significant limitations, particularly regarding fixed text categories and the inability to process diverse data types.
The Generative AI Revolution
Generative AI emerges as a game-changer, allowing organizations to delve deeper into data without the drawbacks of costly data annotation or extensive model training. Its prowess lies in intelligent document processing (IDP), where it can handle a variety of data types, including numeric scores (like sentiment) and free-text summaries. This versatility broadens the horizon for businesses aiming to make sense of large swathes of unstructured content.
Enter Amazon Bedrock Data Automation
Amazon Web Services (AWS) has recently launched general availability for Amazon Bedrock Data Automation, a feature designed to streamline the generation of insights from unstructured multimodal content—be it documents, images, videos, or audio. This service provides pre-built capabilities for IDP, offering a unified API that eliminates the need for complex prompt engineering or laborious fine-tuning. Its simplicity and ease of use make it an ideal solution for document processing workflows at scale.
Seamless Operation
At its core, Amazon Bedrock Data Automation manages document parsing, context management, and model selection automatically. This allows developers to shift their focus from intricate IDP implementation details to more strategic business logic. This capability proves invaluable for organizations looking to deploy scalable and efficient solutions.
Customization and Flexibility
While Amazon Bedrock Data Automation meets diverse IDP needs, some organizations might require specialized customization. For instance, regulatory requirements may necessitate self-hosted foundation models (FMs), or companies may want to maintain full control over their IDP pipelines. Additionally, certain AWS Regions currently lack Bedrock Data Automation availability. In such situations, builders can rely on Amazon Bedrock FMs directly or utilize Amazon Textract for optical character recognition (OCR).
A Step-by-Step IDP Application
This post outlines a robust, end-to-end IDP application made possible through Amazon Bedrock Data Automation and other AWS services. The application employs infrastructure as code (IaC) to deploy an IDP pipeline, complemented by an intuitive UI for transforming documents into structured tables.
Workflow Breakdown
The user interface allows users to input documents (like contracts or emails) and specify attributes for extraction. The IDP process is orchestrated using AWS Step Functions, enhancing efficiency through parallel document processing. Depending on document type and parsing mode, different AWS Lambda functions come into play, ultimately leading to the extraction of meaningful information.
Prerequisites for Deployment
Users can deploy the IDP solution either from their local systems or via an Amazon SageMaker notebook instance. The deployment steps are clearly laid out in the solution README file, ensuring accessibility for users at varying levels of expertise.
Real-World Applications
To showcase the IDP solution, this post presents two use cases: analyzing financial documents and processing customer emails.
Case Study 1: Financial Document Analysis
In this scenario, users extract key financial metrics from multi-page statements. After uploading a PDF document and defining extractable attributes like “operating profit,” the IDP pipeline swiftly processes the document, allowing users to review extracted data in an easily digestible format.
Case Study 2: Customer Email Processing
This case focuses on extracting information from customer complaint emails. Users can specify various fields to be extracted, such as “customer name” and “shipment delay.” The IDP solution offers the ability to download the results as CSV or JSON, streamlining downstream analytics tasks.
Cost Analysis
Pricing models for AWS services are transparent and depend on factors like document size and processing type. Given two example use cases—with varying document types—the estimated costs demonstrate that Amazon Bedrock FMs offer a cost-effective solution, while Amazon Bedrock Data Automation provides a managed service invaluable for operational efficiency.
Conclusion
Extracting insights from unstructured documents at scale is no small feat. The combination of Amazon Bedrock Data Automation with an intelligent document processing pipeline provides organizations a powerful tool to navigate the complexities of unstructured data, ultimately leading to more informed decision-making.
As the market landscape continues to shift, keep an eye on future developments concerning language models and the inclusion of new features that enhance accuracy and scalability. To take the plunge into intelligent document processing using this solution, visit our GitHub repository and explore the documentation on Amazon Bedrock.
About the Authors
Meet the talented team behind this initiative—experts in the field of AI and ML—who are committed to harnessing generative AI to solve real-world business challenges. With their rich backgrounds, they continue to innovate and push the boundaries of what’s possible in intelligent document processing.
For further reading, check out our GitHub repository and the detailed documentation on Amazon Bedrock to begin your IDP journey today.