Executive Summary
Elastiq partnered with Google Cloud to deploy a computer vision solution for a North American electronics manufacturer, achieving 99.8% precision and recall in PCB component detection while accelerating QA processes by 100x.
Client Profile
A platform provider for creating, automating, and optimizing electronic manufacturing. The client inspects millions of chips annually and sought to enhance quality control through AI-driven defect detection.
Business Problem
Traditional quality assurance faced significant limitations:
Manual Inspection Bottleneck
Labor-intensive inspection processes caused inconsistent results and worker fatigue. Human inspectors struggled to maintain accuracy over long shifts.
Scalability Constraints
Inability to scale with increasing production demands meant choosing between thoroughness and throughput.
Extended Production Cycles
Inspection delays extended time-to-market by multiple days per batch, impacting competitiveness.
Incomplete Coverage
Resource constraints meant only spot-checking portions of each batch rather than comprehensive testing-leaving defects undetected.
Elastiq Solution
We implemented a comprehensive AI-powered inspection system:
Data Preparation
Organized PCB images into labeled folders (“missing”/“present”) for supervised learning, creating a high-quality training dataset from historical inspection data.
Custom Model Training
Leveraged Vertex AI AutoML for custom model training, allowing the system to learn the specific defect patterns relevant to this manufacturer’s products.
Real-Time Deployment
Deployed the trained model to a cloud endpoint for real-time predictions, enabling inline inspection without slowing production.
Production Integration
Established data ingestion pipeline and AI model infrastructure that integrates seamlessly with existing manufacturing systems.
Results
The transformation was comprehensive:
- 99.8% precision and recall in defect detection-exceeding human inspector accuracy
- 100x acceleration of QA process speed enabling 100% inspection coverage
- 10x cost savings from reduced manual inspection and rework
- Scalable, reliable real-time deployment that grows with production
Technical Approach
Why AutoML?
For visual inspection tasks with clear defect categories, AutoML provides:
- Rapid development without requiring ML expertise
- Production-ready models with minimal tuning
- Easy retraining as new defect types emerge
Handling Edge Cases
The system includes confidence thresholds that flag uncertain predictions for human review, ensuring critical defects never slip through while maximizing automation.
Business Impact
Beyond the immediate metrics, AI-powered inspection transformed operations:
- Ship with confidence knowing every unit has been inspected
- Reduce customer returns by catching defects before shipment
- Reallocate expertise from repetitive inspection to process improvement
- Scale without hiring as production volumes increase
Conclusion
By combining Google Cloud’s AutoML capabilities with manufacturing domain expertise, Elastiq delivered an inspection system that exceeds human accuracy while operating at machine speed. The result: better quality, lower costs, and faster time-to-market.