Accelerate intelligent document processing with generative AI on AWS

TL;DR

• AWS introduces the GenAI IDP Accelerator, an open source, serverless solution that blends generative AI with AWS Bedrock Data Automation and foundation models to automate intelligent document processing.
• The accelerator converts unstructured documents into structured data, enabling scalable processing across industries while reducing manual data entry and errors.
• Deployments are facilitated by a CloudFormation template and a modular, pattern-based workflow that can start delivering results in days rather than months.
• Real-world use cases include Competiscan processing tens of thousands of campaigns daily and Ricoh handling large volumes of healthcare documents with high accuracy.
• The project emphasizes a configurable, scalable pipeline with prompts, extraction templates, and validation rules, all built on AWS services with strong security and cost efficiency.

Context and background

Every day, organizations process millions of documents—ranging from invoices and contracts to insurance claims and medical records. A large share of the data in these documents is unstructured, representing untapped value that can transform business outcomes. Traditional manual data entry remains common, as PDFs, scanned images, and forms often require human intervention. This manual approach is slow, error-prone, and difficult to scale to increasing volumes. The landscape for IDP (intelligent document processing) has evolved with the rise of large language models (LLMs) and generative AI. Earlier, template-based extraction and rule-based systems struggled with document variations and complex layouts. Modern AI models can understand document context, handle diverse formats without templates, and achieve high accuracy on challenging extractions. This shift enables organizations to process multiple document types with reduced implementation time and cost. In this context, AWS introduces the GenAI IDP Accelerator as an open source, ready-to-deploy solution designed to shorten the path from proof-of-concept to production. The GenAI IDP Accelerator is a serverless, modular pipeline built on AWS services. It leverages Amazon Bedrock Data Automation for out-of-the-box document processing features and Bedrock foundation models for cases requiring custom logic. The goal is to provide an enterprise-grade starting point that can be quickly adapted to different industries and document types while maintaining security and scalability. For readers and practitioners, this project represents a concrete, production-oriented path to move from prototype demonstrations to industrial-grade document automation with generative AI on AWS. The approach emphasizes maintainability, cost-effectiveness, and an architecture designed to scale to high volumes. Source

What’s new

The GenAI IDP Accelerator is presented as an open source, ready-to-deploy solution that combines modern AI capabilities with AWS reliability. Highlights include:

• A serverless foundation that provides processing patterns built on top of AWS Bedrock Data Automation, delivering rich out-of-the-box document processing features, high accuracy, and straightforward per-page pricing. Source
• Integration with Amazon Bedrock state-of-the-art foundation models (FMs) for handling complex documents that require additional, customized logic.
• A modular pipeline that enriches documents at each stage—OCR, classification, extraction, assessment, summarization, and evaluation—so you can deploy and customize each step independently.
• A configuration-driven design that makes it easy to adapt prompts, extraction templates, and validation rules without touching underlying infrastructure.
• Cloud-native deployment via an AWS CloudFormation template, with a deployment time of roughly 15–20 minutes, after which you receive login credentials for the web interface. Source
• A practical demo flow (Pattern-1) illustrating the default Bedrock Data Automation processing pattern and a path to extend with additional processing patterns.
• Real-world customer outcomes highlighted in the post, including Competiscan’s high-volume campaign processing and Ricoh’s healthcare document transformation use case. Source The project is designed to be compatible with a broader ecosystem, offering a scalable starting point for enterprises to automate document workflows tailored to their needs while maintaining security and cost discipline.

Why it matters (impact for developers/enterprises)

The GenAI IDP Accelerator addresses two persistent realities in document processing: the volume of data and the variability of document formats. By combining generative AI with a serverless, AWS-hosted pipeline, the accelerator enables organizations to:

• Process hundreds to millions of documents with reduced manual effort, unlocking faster insights from unstructured data. The approach aims to achieve higher accuracy on complex extractions and shorten the time to value compared with traditional methods.
• Accelerate time-to-production. Whereas a proof-of-concept can be fragile at scale, the accelerator emphasizes production readiness, including error handling, scalability, and enterprise security considerations.
• Maintain flexibility and cost controls. A modular, pattern-based workflow allows teams to tune prompts, extraction templates, and validation rules for their document types while leveraging per-page pricing and scalable infrastructure.
• Leverage AWS’s security, reliability, and ecosystem. Built entirely on AWS services, the solution offers enterprise-grade scalability and security with a straightforward maintenance model and options to extend to CDK or Terraform in future plans. Source For developers and enterprise architects, the accelerator provides concrete guidance on moving from a demo to a production-ready IDP solution that can handle diverse document types and scale with growing data volumes. It also signals a broader AWS commitment to integrating Bedrock capabilities with production-grade workflows for business processes involving sensitive information and compliance requirements. Source

Technical details or Implementation

The GenAI IDP Accelerator is described as a modular, serverless solution built on AWS services. Core architectural elements include:

• A modular pipeline that enriches documents at each stage—from OCR to classification, extraction, assessment, summarization, and evaluation—so teams can deploy and customize steps independently while preserving the integrated workflow. This design allows for flexible adaptations to different document types.
• Use of Amazon Bedrock Data Automation for rich out-of-the-box document processing features, high accuracy, and straightforward per-page pricing. For more complex scenarios, Bedrock state-of-the-art foundation models (FMs) provide the needed custom logic. Source
• An open source delivery model hosted on GitHub, with the ability to update the stack to the latest release and to build from source if deeper customization or regional deployment is required. The project is designed with a configuration-driven approach that makes it straightforward to adjust prompts, extraction templates, and validation rules without changing infrastructure. Source
• Pattern-1 represents the default Bedrock Data Automation workflow, and the post indicates a plan to add more processing patterns to address additional real-world needs. The architecture illustration showcases this default pattern and how the components connect within the AWS stack. Source Deployment and operation details include:
• Prerequisites: An AWS account with administrator permissions and access to Amazon Bedrock models (and Anthropic models where applicable) on Bedrock. See the source for access details and model considerations. Source
• Deployment via AWS CloudFormation: The provided template provisions the necessary resources; after deployment, you receive an email with web interface login credentials. The stack typically deploys in 15–20 minutes.
• Production workflow guidance: In production, documents are routinely loaded to an S3 input bucket, which automatically triggers processing. There is also guidance for testing without the UI and for updating the deployed stack to the latest release. Source
• Extensibility: You can build the solution from source to support more Regions or code changes, and there are plans to add support for AWS CDK and Terraform deployments. Follow the GitHub repository for updates and join the community to contribute improvements. Source Concrete deployment steps (high level):
• Deploy the CloudFormation stack using the provided template.
• After deployment, access the web interface with credentials received by email.
• Load documents into the S3 input bucket to begin automated processing, or test using the recommended testing workflows.
• Optionally, update your stack to the latest release to incorporate new patterns, fixes, and features. The accelerator has already demonstrated business value in real-world contexts, including Competiscan’s need to process tens of thousands of campaigns daily and maintain a large, searchable archive, and Ricoh’s healthcare document processing use case with high-volume, complex medical documentation. Source

Key takeaways

• GenAI IDP Accelerator provides a tested, production-oriented path to automate unstructured documents using generative AI on AWS.
• The solution is modular, serverless, and configurable, enabling rapid adaptation to different document types and business rules.
• Bedrock Data Automation handles out-of-the-box document processing, while Bedrock FMs handle more demanding, custom scenarios.
• Deployment is streamlined through a CloudFormation template, with an expected 15–20 minute provisioning window and scalable per-page economics.
• Real-world use cases underscore the potential to replace manual data entry with accurate, structured extractions at scale. Source

References

• AWS: Accelerate intelligent document processing with generative AI on AWS