In the past, scheduled maintenance, where engineers would work to accurately assess the conditions of material objects on the manufacturing site, was the norm. With this method, however, potential risks that may elude even the most highly-skilled, seasoned workers can go unchecked. It also tends to require downtime and generates workpiece disposal costs and exorbitant costs for managing maintenance parts. Our solutions keep track of actual equipment conditions while using AI and our unique sensing algorithms to visualize the conditions of production lines and individual equipment in a digital space. By additionally digitalizing tacit knowledge such as the sensitivity and intuition of skilled workers, so that signs of equipment issues that even the most seasoned workers may not be able to catch can be detected before they actually lead to failure, our solutions enable predictive maintenance powered by status monitoring, allowing you to address issues proactively.