What challenges did Example Corp face before AI implementation?

Manual and error-prone procurement workflows causing delays, inaccuracies, and inefficiencies in PO creation, approval, and invoice processing.

Source

ProductionEvidence: Low40/100

Optimize Your Supply Chain Processes with Generative AI on AWS

Use case typeBusiness process automationUpdated Jun 13, 2026

Example Corp tackled inefficient procurement workflows including manual purchase order (PO) creation, approval delays, and invoice processing challenges. The solution leverages Amazon Bedrock Agents with action groups to automate PO creation, approval routing, and invoice processing. Integration with AWS AI/ML services such as Amazon Textract for invoice data extraction, Lambda for function orchestration, S3 for data storage, DynamoDB for data management, and Simple Email Service for email routing. Retrieval Augmented Generation (RAG) pattern is used to query knowledge bases stored in Amazon OpenSearch Serverless to support complex workflows. The AI-powered procurement process reduces manual tasks, errors, and accelerates invoice-to-pay cycles while ensuring compliance. Example Corp's procurement specialist used a chat-based Amazon Bedrock Agent to generate POs, send for approval, and process invoices automatically.

Organization: Example Corp
Industry: Logistics
Location: United States
Published: November 2024

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

Customer: Example Corp
Provider: AWS
Maturity: Production
Linked source: AWS Machine Learning Blog

Improved operational efficiency by automating repetitive procurement tasks

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Procurement automation
2Invoice processing automation
3RAG knowledge base search

Implemented a generative AI solution on AWS using Amazon Bedrock Agents to automate PO generation, approval, and invoice processing workflows.
Used action groups executing Python functions invoked by the Bedrock Agents to handle procurement actions.
Automated data extraction from invoices using Amazon Textract and matching invoices to POs in an automated review process.
Ingested and queried procurement documents in an Amazon OpenSearch Serverless knowledge base using RAG.
Leveraged other AWS services including Lambda, S3, DynamoDB, and Simple Email Service to orchestrate and support end-to-end procurement processing.

Technologies

Amazon Bedrock Amazon SageMaker Amazon Lambda Amazon S3 Amazon Textract Amazon DynamoDB Amazon Simple Email Service

Improved operational efficiency by automating repetitive procurement tasks.

Architecture

Solution uses Amazon Bedrock Agents with action groups to orchestrate large language model execution. Uses Amazon Textract for invoice data extraction, AWS Lambda for function orchestration, Amazon S3 for data storage, Amazon DynamoDB for data management, and Amazon Simple Email Service for email routing. Uses Retrieval Augmented Generation (RAG) on Amazon OpenSearch Serverless for knowledge base queries.

Sources & evidence1

Evidence: Low40/100Evidence strength