Optimize inventory and sales planning for a food franchise with an AI operations system (MLOps) powered by Runway, combining demand forecasting and order optimization with existing management tools.

Develop a brake pad property prediction model that meets UIC international standards, using XAI to make AI results interpretable and accessible to non-experts.

Transform complex manufacturing environments with a Software-Defined Factory (SDF) powered by multi-agent systems that convert unstructured input from field experts into autonomous control and real-time monitoring.

Streamline public sector AI operations with Runway by connecting public data and managing AI models (MLOps) in a Kubernetes (K8s) environment compliant with national security standards.

Develop a vision AI-powered system to streamlines the initial stage of battery inspection, reducing manual workload and improving the overall efficiency of quality assessment across large volumes of data.

Automate the standardization of customer order forms with an AI agent that interprets various formats of Technical Demand Conditions (TDCs) using domain-specific knowledge—significantly reducing manual effort and processing time.

Generate diverse tire tread patterns that balance aesthetics and performance, giving industrial designers full creative and technical control.

Navigate scattered public construction data with a RAG-based search agent powered by a Large Language Model (LLM) to surface the most relevant information with greater speed and accuracy.

Predict methanol residuals in the TAME process using a machine learning model, enabling engineers to determine and inject the required amount of methanol without the need for offline sampling.

Reduce development time and experimentation costs by applying an AI-based property prediction model during the composite resin formulation stage to estimate material properties based on raw material mixing ratios—before physical testing.

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 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.