Predictive Quality with Vision AI
Computer vision on the line that catches defects in real time and predicts quality issues before they spread.
Solution Overview
The Challenge
Manual inspection is inherently inconsistent (it varies by inspector, degrades across a shift, and cannot keep pace with modern line speeds), so defects escape and the quality data collected is too noisy to drive improvement.
- 1Manual inspection inconsistent between inspectors and across a shift
- 2Inspection unable to keep pace with line speed, so coverage is partial
- 3Defects discovered after further value has been added, multiplying scrap cost
Our Approach
CloudGate deploys custom vision models trained on your own defect imagery, running inference on Azure IoT Edge at line speed so every unit is inspected without slowing production.
- Custom vision models trained on your own defect imagery and classes
- Edge inference at full line speed with no throughput penalty
- Early drift detection flagging quality issues before defects appear
Typical Outcomes
Typical outcomes for this solution pattern, not the results of a named client engagement.
Technologies Used
More Success Stories
View all case studies
Vehicle Quality Early Warning from Warranty and Telemetry
Detect emerging quality issues weeks earlier by combining warranty claims, dealer narratives and vehicle telemetry, and scope the affected population precisely enough to contain it.
Production Scheduling and Sequencing Optimization
Generate feasible production schedules against real constraints in minutes, and let planners compare objectives before committing rather than defending a plan built in a spreadsheet.
Manufacturing Energy and Emissions Management
Measure energy and emissions per line, per shift and per unit produced, so reduction targets are pursued against evidence rather than against the monthly utility invoice.