Skylark Labs deployed AI video analytics across UltraTech Cement's conveyor belt systems, replacing manual bag counting with real-time automated tracking. Powered by the Kepler platform and Sentinel AI Cameras, the system processes every frame to count, track, and flag anomalies without interrupting production.
Cement conveyor lines process thousands of bags per hour. Manual counting and monitoring could not keep pace, leading to inventory errors and security gaps. High throughput exceeded the capacity of manual counting methods, and different cement types required flexible handling configurations that human operators struggled to manage consistently.
Labor-intensive processes drove up operational expenses while limited surveillance left loading areas exposed to unauthorized access. According to the World Economic Forum, physical AI in supply chains is now critical to realizing operational efficiency. The integration of AI, IoT sensors and predictive analytics is one of the most important trends for 2026 in conveyor systems.
"When you can count every bag in real time with near-perfect accuracy, the entire supply chain starts running differently."
An AI video analytics layer deployed across existing conveyor cameras. The system processes every frame to count, track, and flag anomalies without interrupting production. Real-time counting at line speed achieves near-zero error rate, while operators see throughput, counts, and alerts in a single interface powered by Kepler.
Adaptive configuration is a key differentiator. The Synapse AI Box adjusts automatically when switching between product types or belt speeds, ensuring uninterrupted monitoring regardless of operational changes. The same camera feed powers both counting and security monitoring through Sentinel AI Camera integration.
Every bag movement is automatically logged with timestamps and location data, so the output is not just a count but a complete audit trail ready for integration into warehouse management systems.
The deployment at UltraTech delivered measurable gains across all key metrics: 65% more accurate counting eliminated inventory discrepancies, 75% faster processing removed bottlenecks at loading stations, and a 50% reduction in labor costs freed workers for higher-value tasks. Real-time monitoring also reduced security incidents by 70% across loading zones.
As noted by industry analysts, machine learning algorithms now achieve 99% accuracy in identifying manufacturing defects through computer vision, validating the approach taken in this deployment.
This deployment at UltraTech demonstrates that AI video analytics can transform legacy conveyor operations without replacing existing infrastructure. The system adapts to new conditions in real time, delivering measurable gains in accuracy, throughput, and security. Skylark Labs continues to expand this capability across industrial operations worldwide.
See how AI-powered conveyor analytics can transform your operations.
Schedule a Demo
