Our Technology
Physics-Informed AI for Building Energy Optimization
Unlike black-box ML, our AI understands the physics of your building. It solves thermodynamic equations — never hallucinates, always respects the laws of physics.
🧠 Physics-Informed Neural ODE
Our core is a Neural Ordinary Differential Equation (Neural ODE) — a deep learning architecture that solves continuous-time differential equations. Unlike standard neural networks that learn correlations, our model learns the actual thermodynamic behavior of your building.
Pre-Trained on Simulations
The model arrives 95% ready — trained on thousands of EnergyPlus & OpenModelica simulations before touching your real system.
Constrained by Physics
Every prediction respects conservation of energy, heat transfer laws, and thermodynamic constraints.
1–3 Day Calibration
Shadow Mode fine-tunes the model to your specific building. Zero risk — proves value before going live.
⚡ Model Predictive Control (MPC)
The Digital Twin feeds into our MPC optimizer — a mathematical framework that computes the optimal HVAC schedule every 15 minutes, looking 12 hours ahead.
12-Hour Forecast
Integrates GFS/ECMWF weather models with per-zone solar gain calculations by orientation (N/S/E/W).
Dynamic Pricing
Shifts thermal loads to off-peak hours. Pre-charges building’s thermal mass during cheap periods — uses it as a free battery.
Occupant-Aware
Maintains strict comfort bounds (±0.5°C MAE) while minimizing energy cost. Never sacrifices comfort for savings.
🔒 Edge-First Architecture
All computation runs on the local edge gateway. Your building data never leaves the site.
See It In Action
Book a 30-minute demo to see how our AI works with your building type.
Book a Meeting