AI Workflows for Quality Operations
Master the implementation of automated "Review Agents" and Vision-AI protocols to transition from manual sampling to 100% autonomous quality oversight. Modules empower you to deploy real-time batch monitoring, architect AI-driven compliance documentation, and secure your facility against scrap-heavy production cycles and regulatory bottlenecks.
Start Dates
23 March 2026
23 April 2026
25 May 2026
Level
Intermediate
Duration
16 hours total
2 full days (on-site)
Language
English or German (customer's choise)
Course Fee
CHF 2450 (on-site)
(including all materials and certificate)
Certificate
Industrial AI Prompt Engineer
Course Outline
This 16-hour intensive lab deconstructs the constraints of manual sampling in regulated environments (Pharma, MedTech, Food).
The curriculum provides a standardized framework for deploying edge-based Vision-AI and "Review-by-Exception" agents, enabling quality leads to automate the verification of batch records and lab results while maintaining strict GMP/ISO compliance and technical sovereignty.
The Problem Solved
Eliminating Inspection Bottlenecks: We resolve the constraints of manual sampling in regulated environments. We provide the technical framework required to move to 100% automated, AI-driven inspection for Pharma, Food, and MedTech sectors.
Key Skills & Competencies
1. Computer Vision Deployment: Training edge-models to spot defects in milliseconds.
2. Automated Document Review: AI-driven verification of lab results vs. GMP standards.
3. Scrap Reduction Logic: Using AI to predict and prevent batch drift
Course Modules
11. Vision-AI Foundations.
2. Dataset Engineering.
3. The Compliance Agent.
4. Batch Consistency Monitoring.
5. Edge Hardware Setup.
6. Optical Inspection Lab (Day 2).
7. Document Automation Lab.
8. Reporting & Audit Trails.
9. Scaling Quality AI.
10. Validation & GMP Compliance.
Learning objectives and format
By the end of the course, you will be able to master the implementation of automated "Review Agents" and Vision-AI protocols.
You’ll navigate ten modules covering automated inspection, batch-record verification, and GMP-compliant reporting, transforming your quality department into a proactive, data-driven oversight center.
Key Objectives
- Engineer Vision-AI models to detect surface defects and packaging errors in real-time
- Architect "Review-by-Exception" agents to automate 100% of batch record verification
- Configure edge hardware (cameras/lighting) to ensure reliable data in high-vibration areas
- Implement automated reporting workflows that satisfy FDA/EMA and ISO audit requirements
- Predict batch drift using AI time-series analysis to reduce scrap before errors occur
- Establish a validated "Industrial AI Audit Trail" for proprietary quality data
Learning Format
- Optical Inspection Lab (3 hours): Hands-on setup of cameras and lighting to train a defect-detection model on real physical parts.
- Document Agent Sandbox (3 hours): Build a "Review of Records" agent that cross-references lab PDFs against ISO/Pharma standards.
- PLC-to-AI Trigger Training: Visualize the hardware handshake between a production line sensor and the AI inference engine.
- Audit-Trail Architecture: Draft a compliant, tamper-proof reporting workflow for regulatory bodies.
- Batch Drift Simulation: Analyze real-time production data to identify where a "Good Batch" starts turning into scrap.
- Final Assessment: Earn the Industrial Quality AI Specialist certificate.
The 2-Day Curriculum
Day 1: Vision-AI & Hardware
- Vision-AI Foundations
- Dataset Engineering
- Edge Hardware Setup
- Batch Consistency Monitoring
- Implementation Sprints
Day 2: Documentation & Compliance
6. The Compliance Agent
7. Document Automation Lab
8. Reporting & Audit Trails
9. Scaling Quality AI
10. Validation & GMP
Who is this course for?
Prerequisites
Proficiency with industrial quality standards (GMP/ISO). Completion of IoT & Sensor Basics is highly recommended.
Target Audience
Quality Ops Leads, Lab Technicians, and Validation Engineers in the Pharma, Food, and MedTech sectors.
