The challenge
The client operated across fragmented infrastructure systems with limited visibility into real-time energy production, consumption, and asset behavior.
Operational teams faced challenges including:
- Delayed anomaly detection across solar and energy assets
- Limited forecasting capabilities for energy generation and demand
- Manual monitoring across distributed infrastructure
- Inconsistent reporting from smart meters and IoT devices
- Difficulty optimizing battery storage and energy distribution
The organization needed a scalable platform capable of ingesting and processing high-volume time-series data while supporting predictive analytics and operational decision-making.
What we built
We designed and implemented a personalized recommendation system powered by machine learning models.
The solution included:
- Integration of user interaction, behavioral, and transactional data
- Recommendation models combining collaborative filtering and behavioral signals
- Real-time scoring and content ranking capabilities
- Integration with front-end systems to deliver dynamic recommendations
The system was designed to continuously adapt to user behavior and improve over time.
