Automated EDI Error Detection with GenAI
A mid-market manufacturer processing 50,000 EDI X12 transactions monthly through SAP needed to eliminate 1,000 hours of manual error correction per month. EFS Networks deployed a confidence-gated autonomous agent that achieved 97.3% auto-fix accuracy with 108x ROI.
About the Customer
A mid-market manufacturing company operating 4 plants in the Eastern US, processing approximately 50,000 EDI X12 transactions monthly through SAP ERP. The company exchanges Purchase Orders (850), Invoices (810), and Advance Ship Notices (856) with 200+ trading partners.
Customer Challenge
3–5% of inbound EDI transactions (1,500–2,500 records/month) arrived malformed — missing required fields, invalid qualifier codes, incorrect date formats, mismatched control counts, and truncated segments.
Manual correction consumed approximately 1,000 hours monthly across 3 EDI coordinators, with each fix taking 25–40 minutes. Detection happened only twice daily via batch processing, creating a 4-hour average delay in PO processing. Worse, 8% of manually corrected records introduced secondary errors.
Partner Solution
EFS Networks deployed a confidence-gated autonomous agent using an event-driven pipeline architecture:
5-Step Pipeline with Step Functions Express
- Trading partners submit EDI to S3 landing zone; EventBridge triggers real-time processing (eliminating the 4-hour batch delay)
- Parser Lambda dynamically detects separators and extracts structured segment data
- Classifier Lambda retrieves X12 specifications from Bedrock Knowledge Bases (RAG over EDI specs via OpenSearch Serverless + Titan Embeddings V2)
- Fix Generator produces corrections; dual-layer validation confirms integrity (structural re-parsing + Bedrock Guardrails semantic checks)
- Pattern Learner feedback loop: after 5+ successful human resolutions, patterns earn auto-fix eligibility
7 Lambda Functions
Parser • Classifier • Fix Generator • Fix Validator • Resubmitter • Escalation Handler • Pattern Learner
Confidence-Gated Autonomy
Patterns with ≥95% confidence (validated through multiple human resolutions) are auto-fixed. Unknown patterns are escalated with AI-generated diagnostic reports. The system starts conservative and earns autonomy as patterns are validated — only fully validated corrections reach SAP.
Results and Benefits
| Metric | Result |
|---|---|
| Auto-Fix Accuracy | 97.3% — zero incorrect corrections submitted to SAP |
| Detection-to-Correction (p95) | 12 seconds (eliminated 4-hour batch delay) |
| Auto-Fix Rate (Month 3) | 84%, increasing with pattern catalog growth |
| Manual Hours Eliminated | 840 hours/month (84% of previous workload) |
| Secondary Error Rate | 8% → 0% |
| System Availability | 99.95%; zero SAP modifications required |
| Escalation Diagnostic Quality | 94% rated “useful” by analysts |
| Monthly Savings | $37,800/month at ~$350/month operating cost |
| ROI | 108x |
AWS Services Used
Amazon Bedrock (Claude 3.5 Sonnet), Bedrock Knowledge Bases, Bedrock Guardrails, OpenSearch Serverless, Amazon Titan Embeddings V2, AWS Step Functions (Express), AWS Lambda, Amazon DynamoDB, Amazon S3, Amazon EventBridge, Amazon SNS, Amazon API Gateway, AWS KMS, Amazon CloudWatch. Infrastructure via CDK (Python).
Architecture Overview
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