Quickly train and deploy AI models to automatically detect production defects—no technical expertise required.
Start quickly with a guided user interface on Google Cloud or our API
Train high-precision, domain-specific AI models to detect the tiniest defects
Minimal labeling effort due to active learning; start with only a few defect images
Run prediction models at the production line through Docker containers
Deploy high-performance inspection models at the network edge or on your factory floor.
Deliver significant ROI by reducing inspection costs, rework, and scrap and improving key quality metrics (e.g. escape rate, overkill rate, and yield).
Detects even the most subtle defects at various stages of the assembly process (wrong, misplaced, missing, rotated, or deformed components).
Locates even tiniest and most complex defects (dents, scratches, cracks, deformations, etc.) on any kind of surface.
Run models right on your shop floor with easily deployed Docker containers. Models are trained to meet your production quality requirements for escape and overkill rates.
Start building models with only a few labeled images. Active learning will automatically suggest additional images for the operator to label and further improve the model’s performance.
Visual Inspection AI has been purpose-built for the production environment and addresses a wide range of use cases across the automotive, electronics, semiconductor, and industrial sectors.
Automotive manufacturers use Visual Inspection AI to inspect robot-welded seams for anomalies at the most critical structural joints of the chassis.
Electronics manufacturers use Visual Inspection AI to simultaneously inspect dozens of individual components on high volume printed circuit boards (PCBs) to detect missing, misplaced, or damaged components, screws, springs, and soldering issues.
Semiconductor manufacturers use Visual Inspection AI to detect and locate wafer defects, chip defects, or die cracks.