The ceramics industry stands at the brink of a fundamental shift in how quality control is conceived, implemented, and scaled.

For decades, the promise of artificial intelligence-powered inspection systems was held back by a persistent obstacle: the scarcity of high-quality data. This bottleneck made it difficult for ceramics manufacturers to realize the full potential of advanced machine learning, particularly because product variations are abundant while defects tend to be rare, subtle, and highly variable due to the nature of ceramic materials and processes.