Minimize production line downtime by using AI to detect anomalies in ventilation fan motors within the automotive paint shop.

Develop a secure, closed-network AI platform to detect model degradation and automate retraining, enhancing combat readiness and operational efficiency.

Automate data building with auto-annotation and optimize HPC infrastructure resources, all on a single AI platform (Runway), to streamline AI operations in the electronics industry.

Automate operational processes, including retraining-redeployment, by utilizing various image analysis models on the AI platform, Runway.

Build an AI system leveraging real-time PLC data from the manufacturing floor to easily integrate diverse data features, use high-resolution data, and establish a sustainable AI process.

Optimize discount pricing for fresh produce using AI, increasing profit by 3% per store. Expand the AI-validated model to multiple locations.

Optimize furnace equipment temperature with AI-powered Model Predictive Control (MPC) to achieve consistent material quality and minimize energy consumption.

Optimize scheduling for multi-variety, small-batch production to reduce bottlenecks, minimize idle equipment, and boost schedule adherence by 66%.

Reduce energy costs by 11% using reinforcement learning to implement optimized control strategies, minimizing variation among site managers.

Reduce downtime in semiconductor post-processing with a deep learning-based anomaly detection model for CO2 laser drills.

Reduce production line downtime from motor failures with an AI-driven MLOps platform and advanced data collection for efficient machinery oversight.

Enhance motion control accuracy with data-driven simulators and reinforcement learning for automated parameter tuning, minimizing the gap between commanded and actual values.

Leverage reinforcement learning to optimize electronic component mounting sequences on PCBs, reducing overall process time in SMT.

Predict critical failures and minimize downtime in an automotive assembly process with over 10,000 robotic arms by implementing anomaly detection within an AI operational environment (MLOps).

Automate multi-robot chassis welding routes and tasks using AI and robotics, leading to a substantial reduction in labor hours and product production time.

Create an AI simulator using real-world electric vehicle data to optimize vehicle energy control with a lightweight model for enhanced computational speed.

Boost operational efficiency with AI-based simulators and reinforcement learning agents for automated optimal component placement in custom ASICs.

Develop an AI operational environment using ensemble models to improve predictive accuracy, operate across 787 power plants, and streamline retraining and deployment of 3,000+ models.

Implement a deep learning-based anomaly detection model in a polypropylene reactor to predict emergency shutdowns and analyze anomalies.

Leverage battery management system data and electric vehicle(EV) driving patterns for predicting lithium battery residual life and lifecycle monitoring.

Predict and monitor anomalies with ESS anomaly detection models, designed to foresee and track abnormalities before a fire occurs.

Predict anomalies and time to failure with anomaly detection models that can respond quickly to recipe changes using minimal data from semiconductor production equipment.