What challenges did Public Sector face before AI implementation?

Manual processing of tax forms, medical records and other documents caused delays, errors, and lacked transparency in government workflows. Risk of human data entry mistakes, misplaced documents, compliance issues, and inefficient storage were significant. High operational costs and inability to scale with growing volumes frustrated both employees and citizens.

Source

ProductionEvidence: Low40/100

Transforming government application systems using intelligent document processing on AWS

Use case typeIntelligent document processingUpdated Jun 13, 2026

Government agencies are overwhelmed by manual processing of large volumes of bureaucratic documents, leading to slow processing times, data entry errors, lack of transparency, inefficient storage, compliance risks, and high operational costs. AWS implemented an Intelligent Document Processing workflow integrating Amazon Textract for text extraction, Amazon Comprehend for document classification, and Amazon Augmented AI for human review of low-confidence cases. The system uses Amazon S3 for document storage, Amazon SQS for queuing, AWS Lambda for orchestration, Amazon SNS and SES for notifications, and Amazon DynamoDB for storage of final results. Human reviewers are integrated in the loop to ensure accuracy and quality control. The solution has streamlined bureaucratic workflows, reduced errors, sped approvals, enhanced transparency, and improved service delivery to citizens.

Organization: Public Sector
Industry: Public Sector
Location: United States
Published: January 2025

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Public Sector
Provider: AWS
Maturity: Production
Linked source: AWS Blog

Customer identity supportedSource describes one deploymentMaturity supported

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Use case focus

Showing 2 of 2

1Intelligent Document Processing
2Workflow Automation

Implemented an end-to-end Intelligent Document Processing workflow using AWS AI/ML services.
Uploaded documents trigger Amazon S3 events, which queue processing tasks via Amazon SQS.
Lambda functions orchestrate calls to Amazon Textract for text extraction and Amazon Comprehend for classification.
Low confidence classification results are routed for human review using Amazon Augmented AI (A2I).
Amazon SNS and SES provide real-time notifications and alerts to stakeholders.
Processed data is stored in Amazon DynamoDB for quick retrieval and transparency.

Technologies

Amazon Textract Amazon Comprehend AWS Lambda Amazon Simple Storage Service (S3)Amazon Simple Queue Service (SQS)Amazon Simple Notification Service (SNS)Amazon Augmented AI (A2I)Amazon Simple Email Service (SES)Amazon DynamoDB

Architecture

The solution architecture uses Amazon S3 for document storage, Amazon SQS for queuing, AWS Lambda to process messages and invoke AI services, Amazon Textract to extract text and data from documents, Amazon Comprehend for document classification, Amazon Augmented AI for human review, Amazon Simple Notification Service (SNS) and Amazon Simple Email Service (SES) for stakeholder notifications, and Amazon DynamoDB to store the final processed results.

Sources & evidence1

Evidence: Low40/100Evidence strength

Customer explicitly identified
Deployment status explicitly supported
Technical implementation details available

Type: Blog PostPublished: Jan 28, 2025Publisher: AWS BlogEvidence: VendorConfidence: Medium

Primary source

AI-generated summary. Verify important details with the linked sources before relying on this case.

Transforming government application systems using intelligent document processing on AWS

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