Savantium, a Crowley Tech venture, applies physics‑informed AI neural networks to optimize energy use in utilities, grid operators, and data centers. By integrating its power‑generation expertise, the platform reduces electricity costs and improves efficiency for large‑scale compute facilities that now function as de‑facto power companies.
Funding
Funding not disclosed
Founders
Product
Problem
Utilities, grid operators, and data centers often manage complex energy systems with legacy control software that cannot dynamically balance loads, leading to inefficiencies, higher operating costs, and increased carbon emissions.
Solution
Savantium delivers physics-informed AI neural networks that integrate directly with existing energy control systems to provide real-time optimization of power generation and consumption. By embedding domain-specific physical models into machine-learning architectures, the platform predicts load patterns, identifies waste, and recommends adjustments that balance supply and demand across the grid or data‑center infrastructure. Operators receive actionable insights through a dashboard that quantifies cost savings, emission reductions, and performance metrics, enabling continuous improvement without replacing current hardware. The solution is designed to scale across utilities, grid operators, and large‑scale data centers worldwide, turning AI‑driven compute workloads into an asset rather than a liability.
Target Audience
Primary customers are utility companies, regional grid operators, and large data‑center operators seeking to improve energy efficiency and reduce operational costs.
Features
- Physics-informed neural network models that combine power‑generation theory with data‑driven learning for accurate load forecasting
- Real-time integration with SCADA, EMS, and data‑center power‑management systems via standard APIs
- Automated optimization engine that adjusts generation set points, cooling loads, and workload placement to minimize waste
- Auditable analytics dashboard showing cost, efficiency, and emissions impact with drill‑down capabilities
- Adaptive learning loop that continuously refines models based on operational feedback and changing demand patterns