FDA-Compliant QA Agent for Medical Device Manufacturing

The Problem

In medical device manufacturing, a misprint on a label isn’t a minor issue—it’s a potential recall, an FDA warning letter, or worse. A life sciences startup was relying on manual visual inspection, catching only 98% of defects. That 2% gap represented millions in potential liability and regulatory risk.

The Architecture

flowchart TB
  subgraph capture [Assembly Line Capture]
      Camera[Line Cameras]
      ImageQueue[Image Queue]
  end
  
  subgraph ocr [OCR Pipeline]
      DocAI[Google Document AI]
      GPT4V[GPT-4o Vision]
      TextExtract[Text Extraction]
  end
  
  subgraph validation [Regulatory Validation]
      Guardrail[Guardrail Agent]
      FDA[FDA 21 CFR Part 11 Rules]
      Specs[Product Specifications]
  end
  
  subgraph output [QA Output]
      Pass[Pass - Continue Line]
      Reject[Reject - Divert]
      AuditLog[Audit Trail]
  end
  
  Camera --> ImageQueue
  ImageQueue --> DocAI
  DocAI --> TextExtract
  ImageQueue --> GPT4V
  GPT4V --> TextExtract
  
  TextExtract --> Guardrail
  FDA --> Guardrail
  Specs --> Guardrail
  
  Guardrail -->|"Compliant"| Pass
  Guardrail -->|"Non-Compliant"| Reject
  Guardrail --> AuditLog

Regulatory Guardrail Agent

Every device that rolls off the line passes through a multi-stage validation:

1. Dual OCR Pipeline: Google Document AI handles structured text while GPT-4o Vision catches edge cases—rotated labels, glare, partial occlusion
2. Guardrail Agent: Cross-references extracted text against FDA 21 CFR Part 11 requirements and product specifications. Checks include:
   • Lot number format compliance
   • Expiration date validity
   • UDI (Unique Device Identifier) accuracy
   • Label content completeness
3. Audit Trail: Every inspection—pass or fail—is logged with full traceability for FDA audit readiness

The system operates at line speed with sub-second latency per unit.

Tech Stack

• Google Document AI — High-accuracy structured OCR
• GPT-4o Vision — Edge case handling and visual reasoning
• Python — Real-time processing pipeline
• Custom MLOps — Model monitoring and drift detection

The Impact

Since deployment, zero non-compliant devices have shipped. The complete audit trail has turned FDA inspections from anxiety-inducing events into routine reviews.