Stop Managing Energy.
Start Mastering It.
Harl Energy models your data center as a Reinforcement Learning environment. Our AI agents find efficiencies you never thought possible.
>System Online
Live Systems
REAL-TIME
AGENTS_ACTIVE
4096
CURRENT PUE
1.14
MW_OPTIMIZED
12.4
SIMULATIONS/SEC
500k+
UPTIME: 99.97%
Model. Learn. Optimize.
Offline RL trained on historical data → Physics-based simulator → Heterogeneous agents for multi-objective optimization
>Heterogeneous Agent System
[AGENT_01]Cooling
Dynamic HVAC optimization based on workload + weather
[AGENT_02]Scheduling
Thermal-aware GPU job placement to maximize utilization
[AGENT_03]Grid
Energy arbitrage + demand response optimization
OPTIMIZATION: MFU × PUE × $/kWh × Carbon Intensity
Multi-Objective Reward
GPU Utilization (MFU)Max
Energy Efficiency (PUE)Min
Cost ($/kWh)Min
Carbon IntensityMin
>Deployment Pipeline
01Offline Learning
Train RL agents on historical telemetry data—no live system interaction.
20% fan energy savings, 4% water reduction
02Digital Twin Validation
10,000+ simulated episodes covering weather extremes, workload spikes, equipment failures.
<10% prediction error
03Supervised Deployment
Advisory mode → Supervised autonomy with human oversight for edge cases.
50-80% autonomous actions