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" Through a strategic partnership, we conducted in-depth feasibility studies using mixed-integer programming (MIP) and predictive forecasting powered by large language models (LLMs). These advanced techniques allowed us to optimize complex infrastructure scenarios and predict demand patterns with high accuracy, delivering actionable insights for our energy sector clients. By applying cutting-edge AI-driven methodologies, we were able to transform how our clients approached their operations, providing them with the tools to make smarter, data-backed decisions that enhanced their efficiency. The results significantly improved operational performance and long-term strategic planning, empowering clients to make informed choices that drove substantial cost savings. By seamlessly integrating AI-driven forecasting with sophisticated optimization models, we helped clients streamline resource allocation, reduce inefficiencies, and build resilience in an energy landscape that’s rapidly evolving. This partnership ultimately allowed clients to navigate uncertainties more effectively while enhancing their ability to adapt to future challenges. "

Zuse Institute Berlin (ZIB)

Open Grid Europe (OGE)

" The predictive failure analysis project for ABB car chargers transformed maintenance operations by leveraging machine learning and real-time telemetry. By analyzing voltage fluctuations, temperature spikes, and error patterns, the team developed AI models that detected faults before they led to charger failures. Using advanced time-series forecasting and anomaly detection, they enabled proactive servicing, reducing downtime and preventing costly disruptions. Once deployed, the system delivered real-time predictive alerts, cutting unexpected failures and optimizing maintenance schedules. This resulted in a significant boost in charger reliability, lower servicing costs, and a seamless user experience for EV drivers. With continuous model refinement, the solution became even more accurate over time, ensuring ABB’s charging network operated at peak efficiency. "

Microsoft Corporation

Asea Brown Boveri (ABB)

" In close collaboration with GSK, we combined probabilistic methods with real-world data to simulate how diseases spread and how seasonality influenced outbreaks. By integrating these advanced techniques, we were able to create more accurate models that helped GSK refine their forecasting and optimize vaccine distribution strategies. The outcome was a powerful tool that enabled GSK to predict infection trends with greater precision and allocate resources more effectively, ensuring that vaccines were deployed in the right places at the right times. These insights gave GSK the flexibility to adjust their strategies in real-time, allowing them to stay ahead of emerging patterns and improve overall disease prevention efforts. The collaboration ultimately contributed to more targeted interventions, supporting GSK in their mission to enhance public health and respond to evolving infection dynamics. "

Max-Planck Institute

GSK (formerly GlaxoSmithKline)