Reducing energy costs by predicting and controlling efficiency, saving up to 35% and enabling anomaly detection and automated management for commercial properties.

Developing a comprehensive solution powered by sentiment analysis, machine learning algorithms, and trading strategies for better-informed trading decisions.

Churn prediction model for business customers that identified potential churners with higher precision than the client’s existing model, achieving a 15-20% improvement in accuracy.