Agentic AI for EDI Error Detection: 108x ROI in Manufacturing
A mid-market manufacturing company processing 50,000 EDI transactions monthly needed to eliminate the 1,000 hours spent manually correcting 3-5% of malformed inbound records.
The Challenge
Approximately 1,500-2,500 EDI X12 transactions arrived malformed each month — missing required fields, invalid qualifier codes, incorrect date formats. Each record required manual intervention:
- Detection in SAP error queue (batch process, 2x daily)
- Diagnosis using X12 specifications
- Manual segment-by-segment correction
- Resubmission and verification
Result: 25-40 minutes per record, 1,000 hours monthly labor, 4-hour average delays in PO processing, and 8% of manual corrections introduced new errors requiring rework.
Technology Stack
Amazon Bedrock (Claude 3.5 Sonnet), Bedrock Knowledge Bases with OpenSearch Serverless, AWS Step Functions (Express), AWS Lambda (7 functions, ARM64), DynamoDB pattern catalog, EventBridge event-driven architecture, and AWS CDK for infrastructure as code.
Zero modifications to the existing SAP system — integration via S3 landing zone and archive bucket polling.
AWS Partner Validation
This case study is part of EFS Networks' AWS Agentic AI Competency submission. View our validated case studies on the AWS Partner Network.
Key Capabilities Demonstrated
Problem Category: Agentic AI, Enterprise Automation, EDI Processing
AI Models Used: Claude 3.5 Sonnet with confidence-gated autonomy
Automation Pattern: Event-driven Step Functions, confidence-based escalation, RAG-enhanced diagnosis
Architecture: S3 event triggers, multi-stage validation, DynamoDB pattern catalog, OpenSearch Serverless
AWS Services: Bedrock, Knowledge Bases, Step Functions Express, Lambda (ARM64), DynamoDB, EventBridge
Business Outcome: 108x ROI, 1,000 hrs/mo saved, 12s processing, 97.3% accuracy
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