AI computer Vision For
Quality Inspection
Automate defect detection with real-time visual intelligence.
Modern manufacturing demands speed, precision, and consistency, especially when it comes to quality control.
AI Computer Vision enables automated, real-time defect detection and classification in manufacturing.
This no-code solution empowers non-experts to train and deploy models easily, providing centralized quality control across production lines without requiring changes to existing systems.
Waste and Avoid Recalls at All Costs
Human Error and Inconsistent Inspection
High Labor Costs and Operator Dependency
Production Bottlenecks from QA Delays
Limited Visibility and Traceability
Manufacturing Pain Points:
Defect Detection Accuracy
>
0
%
>
0
%
Reduction in QA Labor Costs
0
%
Adaptable to Any Production Environment
0
%
Real-Time Visibility and Alerts
0
%
Compatibility with Existing Systems
What benefits can you expect?
Case Studies
Doga sought to eliminate manual inspection and implement a fully automated vision-based defect detection system on its metal parts lines. The ultimate goal was to fully remove human intervention in quality inspection while preparing the system for future enhancements, such as automatic side flipping and the arrangement of conforming parts.
AI computer Vision For AI Quality Inspection
Frequently Asked Questions
Is the system compatible with our existing cameras and infrastructure?
Yes. The solution is designed for easy integration with most standard industrial cameras and PLCs. There’s no need to invest in specialized hardware.
Do we need AI experts or data scientists to use this solution?
No. The platform is completely no-code. Operators can upload images, label data, train models, and monitor predictions using an intuitive interface—no technical expertise required.
Can we scale the system across multiple lines or factories?
Absolutely. The solution supports centralized monitoring across multiple production lines, factories, and regions. It’s built for scale.
What accuracy can we expect?
You can expect >99.99% accuracy for defective part detection and >97% overall classification accuracy when models are properly trained.