Automated EDI Error Detection & Correction Using Amazon Bedrock
AWS Partner
EFS Networks
AWS AI Competency
Agentic AI Consulting Services
ABOUT THE CUSTOMER
A mid-market manufacturing company operating 4 plants in the Eastern United States, processing approximately 50,000 EDI (Electronic Data Interchange) X12 transactions per month through their SAP ERP system. The organization exchanges Purchase Orders (850), Invoices (810), and Advance Ship Notices (856) with over 200 trading partners. EDI processing is mission-critical – malformed records block purchase order intake, invoice reconciliation, and shipment notifications, directly impacting revenue and supply chain operations.
CUSTOMER CHALLENGE
Approximately 3-5% of inbound EDI transactions (1,500-2,500 records per month) arrived malformed – missing required fields, invalid qualifier codes, incorrect date formats, mismatched control counts, or truncated segments. Each malformed record required manual intervention by one of three EDI coordinators who would diagnose the error, cross-reference X12 specifications and trading partner documentation, manually edit the raw EDI segment-by-segment, resubmit to SAP, and verify acceptance.
This manual process consumed approximately 1,000 hours per month across the team, with each correction taking 25-40 minutes. Detection was batch-based (twice daily), introducing an average 4-hour delay in purchase order processing. Worse, approximately 8% of manually corrected records introduced new errors, requiring a second correction cycle.
The customer needed a system that could detect errors in real-time, classify patterns, apply corrections autonomously for known error types, and escalate genuinely novel errors to human analysts – all without modifying their existing SAP system.
PARTNER SOLUTION
EFS Networks designed and deployed a confidence-gated autonomous agent on AWS that detects, classifies, and corrects malformed EDI records in real-time.
Architecture: The solution uses an event-driven pipeline built on AWS Step Functions (Express) orchestrating 7 AWS Lambda functions (Python 3.12, ARM64/Graviton): Parser, Classifier, Fix Generator, Fix Validator, Resubmitter, Escalation Handler, and Pattern Learner. Amazon Bedrock (Claude 3.5 Sonnet) powers the error classification and correction generation, with Amazon Bedrock Knowledge Bases and Amazon OpenSearch Serverless providing RAG-based retrieval over X12 EDI specifications.
How it works:
1. Trading partners submit EDI documents to an S3 landing zone. Amazon EventBridge triggers the processing pipeline automatically on each upload – replacing the twice-daily batch cycle with real-time processing.
2. The Parser Lambda dynamically detects EDI separators (which vary by trading partner) and extracts structured segment data. The Classifier Lambda queries a DynamoDB pattern catalog and retrieves relevant X12 specification context from the Bedrock Knowledge Base to classify the error type.
3. Confidence-gated autonomy determines the next step: error patterns with 95%+ confidence (established through multiple successful human validations) are auto-fixed. Unknown or low-confidence patterns are escalated with an AI-generated diagnostic report.
4. For auto-fix paths, the Fix Generator Lambda uses Bedrock to produce a corrected EDI document, which undergoes dual-layer validation – structural re-parsing through the EDI parser confirms no syntax errors were introduced, and Amazon Bedrock Guardrails perform a semantic check for PII exposure and structural integrity. Only fully validated corrections are resubmitted to SAP.
5. The Pattern Learner implements a closed-loop feedback cycle: when human analysts resolve escalated cases, the pattern catalog’s confidence score increases. After 5+ successful human resolutions, a pattern earns auto-fix eligibility. This means the system starts conservative and gradually earns autonomy as patterns are validated.
Key AWS services: Amazon Bedrock (Claude 3.5 Sonnet), Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails, Amazon 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 as Code: All resources deployed via AWS CDK (Python) with CDK Nag for automated security validation. Zero modifications to the customer’s SAP system – integration uses the existing S3 landing zone pattern.
RESULTS AND BENEFITS
– 97.3% auto-fix accuracy – zero incorrect corrections submitted to SAP; the validation layer caught the 2.7% that needed regeneration
– 12-second detection-to-correction time (p95), eliminating the previous 4-hour batch delay
– 84% auto-fix rate at month 3, increasing as the pattern catalog grows from ongoing human feedback
– 94% of escalation diagnostics rated “useful” by analysts, including error summary, suggested approach, and similar historical patterns
– 99.95% system availability with zero SAP modifications required
– 840 hours per month of manual correction eliminated (84% of the previous 1,000-hour monthly workload)
– Secondary error rate reduced from 8% to 0% – the dual-layer validation prevents corrections from introducing new errors
– $37,800/month in labor savings at a system operating cost of approximately $350/month, delivering a 108x return on investment
ABOUT EFS NETWORKS
EFS Networks is an AWS Advanced Tier Services Partner (Top 1%) founded in 2005 and headquartered in Philadelphia, PA. With approximately 50 employees and over 10 years of AWS experience, EFS holds AWS Well-Architected Partner, Lambda Delivery Partner, and Serverless Delivery Partner designations. EFS specializes in building production agentic AI solutions on AWS across healthcare, enterprise software, and manufacturing verticals.