The AI-powered Level Monitoring System uses infrared and optical analytics to continuously measure molten metal levels with high precision, delivering real-time visibility, automated alerts, and programmable closed-loop control to stabilize furnace operations.
Reduce Downtime in Steel Plants Predictive Furnace Monitoring to Extend Equipment Life Achieved 95% accuracy in hotspot detection
Achieved a 70% reduction in temperature-related failures with AI-powered monitoring of switchyard assets, ensuring early detection of thermal anomalies and potential equipment faults for enhanced reliability and predictive maintenance.
Achieved 70% Reduction in downtime of conveyor failures with Ripik AI conveyor belt monitoring system. This ensured smoother operations, minimized downtime, and delivered significant cost savings.
Real-time camera intelligence to monitor materials, equipment, and processes, driving efficiency and consistency in industrial operations.
Hosts and manages hundreds of Vision AI Agents, enabling you to customize, deploy, and scale real-time monitoring solutions instantly across operations.
Let us walk you through a tailored demo experience.
The AI-powered Level Monitoring System uses infrared and optical analytics to continuously measure molten metal levels with high precision, delivering real-time visibility, automated alerts, and programmable closed-loop control to stabilize furnace operations.
Reduce Downtime in Steel Plants Predictive Furnace Monitoring to Extend Equipment Life Achieved 95% accuracy in hotspot detection
Achieved a 70% reduction in temperature-related failures with AI-powered monitoring of switchyard assets, ensuring early detection of thermal anomalies and potential equipment faults for enhanced reliability and predictive maintenance.
Achieved 70% Reduction in downtime of conveyor failures with Ripik AI conveyor belt monitoring system. This ensured smoother operations, minimized downtime, and delivered significant cost savings.
Real-time camera intelligence to monitor materials, equipment, and processes, driving efficiency and consistency in industrial operations.
Hosts and manages hundreds of Vision AI Agents, enabling you to customize, deploy, and scale real-time monitoring solutions instantly across operations.
Let us walk you through a tailored demo experience.
AI in the mining industry is not merely a trend; it’s a necessity. With vast operations often spread across remote and hazardous environments, real-time insights and automation are key to minimizing human error, optimizing production, and maintaining sustainability.
Discover how AI is transforming plant uptime in manufacturing by enabling predictive maintenance, real-time anomaly detection, and SOP compliance. Improve equipment reliability, reduce unplanned downtime, and enhance overall operational efficiency
Agentic AI in manufacturing operations are designed, executed, and optimized. These systems act autonomously, making decisions based on real-time data to improve efficiency, reduce costs, and maintain high product quality
Root Mean Square Error (RMSE) is a widely used metric that measures the average magnitude of prediction errors in a model. It calculates the square root of the mean of squared differences between actual and predicted values, providing insight into model accuracy. Lower RMSE values indicate better predictive performance.
The blast furnaces steelmaking process is a complex and requires precise control over various parameters. Artificial Intelligence (AI) is optimizing this process, enhancing both productivity and quality.
AI platforms for anomaly detection are transforming a wide range of industries by leveraging advanced machine learning and deep learning algorithms to proactively identify potential issues, enabling businesses to mitigate risks and improve efficiency.
The role of AI in enhancing energy efficiency in cement plants particularly in fuel Consumption is significant portion of cement production expenses. Real-time monitoring, predictive analytics, and optimization of plays a key role in this.
Vision AI agent operate through a structured pipeline involving perception, analysis, decision-making, and continuous learning. By leveraging computer vision, deep learning, and real-time processing, these agents enable automation, predictive analytics, and intelligent decision-making across industries.
Particle size analysis plays a critical role in heavy industries such as cement, mining, steel, and power plants. Particle size distribution impacts product quality, process efficiency, and overall operational costs.
Conveyor volume scanners are revolutionizing stockpile management by providing precise, real-time data to enhance material flow, inventory control, and operational efficiency. Using advanced technologies like LIDAR and Vision AI, these systems help reduce waste, optimize production, and improve safety across industries such as steel manufacturing, mining, cement, and logistics.
