Autonomous Operation & Safety Assurance
Powered by PhyAI, DeepBot continuously learns and optimizes control strategies while ensuring safe operation through physics-based models.
Level 4.5 System Optimization Agent｜Powered by PhyAI™
DeepBotEdge AI Agent
Powered by the PhyAI™ Engine, DeepBot is an integrated Edge AI Agent for HVAC,
compressed air, and utility systems, enabling rapid deployment, autonomous
identification, and continuous learning for L4.5 AI optimization.
1–2 DaysDeployment Time
5-30%Operational Efficiency Improvement
< 2 YearsPayback Period
7x24Unattended Operation
Product Overview
What is DeepBot?
Plug-and-Play Edge AI Optimization Device
Upgrades HVAC, compressed air, and utility systems with L4.5 autonomous edge intelligence.
From System Optimization to Edge Intelligence
DeepBot is an Edge AI Agent for distributed energy systems. Powered by PhyAI™, it combines physics-based models and reinforcement learning to enable autonomous identification, learning, and real-time optimization for L4.5 intelligent operation.
Lightweight Deployment
Advanced Intelligence
Optimal Energy Efficiency
Adaptive Operation
Edge AutonomyLocal inference and decisions for continuous optimization.
Plug-and-PlayDeploy and optimize within 1–2 days.
Cross-Scenario AdaptabilityBuilt for factories, utilities, and data centers.
Fast ROI5–30% efficiency gains with <2-year payback.
AI Autonomy Levels
DeepBot Edge Agent: L4.5 Autonomous Energy Terminal
From PID control and rule-based automation to Physical AI autonomy, DeepBot bridges L4 system optimization and L5 autonomous intelligence as an L4.5 Edge AI Agent for distributed energy systems.
4 Key Customer Benefits
4 Key Benefits, Making Energy Savings Measurable
From safe operation and rapid deployment to energy savings and ROI, DeepBot enables data-driven decisions with measurable value.
5%–30% Energy Savings
Real-time sensing and AI-driven optimization unlock energy savings with measurable and verifiable results.
Plug-and-Play, Deploy in 1–2 Days
A standardized Edge AI solution with no major modifications, enabling automatic identification, integration, and optimization.
Fast ROI, Payback <2 Years
Lightweight deployment with no major retrofits enables fast validation of energy savings and rapid return on investment.
Applications
3 Key Applications
Factory Utility Systems
Factory Utility Systems
Optimizes HVAC, compressed air, and utility systems with rapid deployment, intelligent optimization, and continuous energy savings across industrial facilities.
ApplicationsCompressed Air System OptimizationCirculating Water System OptimizationNo Production Downtime, Deployment in 1–2 DaysPublic Buildings
Public Buildings
Optimizes HVAC and utility systems across hospitals, transit facilities, and commercial buildings for comfortable operation and continuous energy savings.
ApplicationsHVAC Energy OptimizationHeating Plant OptimizationNo Business Disruption, Deployment in 1–2 DaysData Centers
Data Centers
Optimizes precision cooling, chiller plants, and ventilation systems to improve energy efficiency while maintaining stable environments and system availability.
ApplicationsPrecision Cooling Control OptimizationChiller Plant Energy OptimizationNo Business Disruption, Deploy in 1–2 DaysKey Differentiators
DeepBot vs Traditional Control
From rapid deployment to intelligent optimization and ROI, DeepBot enables scalable Edge AI solutions for faster energy savings.
| Dimension | Traditional BAS / experience-based control | Generic operations SaaS | DeepBot Edge Agent |
|---|---|---|---|
| Deployment | Requires a bespoke retrofit project, complex to implement | Requires cloud onboarding and a constant connection | No retrofit, plug and play |
| Delivery time | 1–3 months | 1–2 weeks | 1–2 days |
| Offline availability | Depends on local rules or a master controller | Depends on a cloud connection | Autonomous at the edge, works offline |
| Data / compute dependence | Mostly dependent on human experience | Heavily dependent on cloud data and compute | Low data dependence, local inference, physics as the backstop |
| Explainability | Rules are understandable but optimization is limited | Weak explainability | Physical models are explainable, strategies verifiable |
| Energy savings | 5-10% | 10-20% | 5-30% |
| Payback period | 3–5 years | 2–3 years | < 2 years |
| Operations model | Requires staffed operation | Platform-assisted operations | 24/7 safe operation, no staffing required |
| Cross-scenario replication | Highly bespoke, hard to replicate | The platform is reusable but adapts poorly on site | Standardized terminal plus physical models, portable and repeatable |
Technology Overview
PhyAI™: Physics Models + Reinforcement Learning
Powered by PhyAI™, DeepBot combines physics-based models and reinforcement learning to deliver edge-based identification, autonomous learning, and real-time optimization, reducing data and cloud compute dependency for plug-and-play L4.5 AI energy optimization.
Auto Identification & Physics-Based Modeling
Automatically identifies equipment and operating conditions, enabling rapid system modeling and parameter configuration through a physics-based model library.
Edge Sensing + Autonomous Learning
Continuously collects temperature, pressure, flow, and energy data on-site, combining physics-based models with reinforcement learning to adapt to real-world operating conditions.
Real-Time Optimization & Autonomous Control
Generates real-time optimization strategies and automatically adjusts equipment parameters for AI-driven autonomous operation.
Cloud-Edge Collaboration & Results Validation
Combines cloud-based data intelligence with edge optimization to improve models and validate energy savings through real-time, measurable results.
System Architecture
Cloud-Edge-Device Collaborative Architecture
Cloud management, edge optimization, and real-time sensing create a closed loop from data to intelligent control.
Customer Cases
Proven Results, Measurable Impact
Li Auto
With DeepCtrls’ DeepBot, Li Auto implemented PhyAI™-powered high-precision modeling and global optimization to improve compressed air system efficiency while ensuring production safety and air quality, achieving 9.3% energy savings.
Contact UsMore case studies
More Customers
Reshaping Global Compute and EnergyInfrastructure with Physical AI
Proprietary PhyAI™ Physical AI Engine
10–40% Energy Savings, Verified in Real Time
390+ Customers Across Multiple Industries