AI-Driven Energy Flexibility
Reduce Your Energy Bill with MBML's AI Agents
We build AI agents for Buildings, DSOs, Solar Production and EV Charging Stations — all communicating with each other to optimize energy flexibility in real time.
The Problem
The EU is accelerating distributed energy resource (DER) deployment, yet the coordination architecture has not kept pace. Millions of small-scale, intermittent renewable assets now overwhelm a grid model designed for centralised dispatch, creating a coordination bottleneck: curtailed generation, underutilised flexibility, and missed prosumer value. Energy communities offer a bottom-up alternative but remain hard to establish: DSOs lack integration tools, smart metering is fragmented, RECs lack software for shared-asset optimisation, and static pricing fails to reflect real-time grid conditions.
The Vision
A neutral hosting layer that connects any energy device through protocol-agnostic bridges, normalizes their data into a unified fabric, and lets autonomous AI agents optimize and trade energy on behalf of asset owners. No vendor lock-in. No static rules. Just intelligent, real-time coordination that unlocks the value of local flexibility.
Key Metrics
The scale of the opportunity in decentralized energy
Potential EU energy savings addressable by intelligent consumption
Target energy cost reduction per building
Four Agent Types
AI agents that communicate with each other to decrease energy waste and reduce electricity bills
Building Agents
Optimize energy usage across commercial and residential buildings with AI agents that manage HVAC, lighting, and storage in real time.
DSO Agents
Coordinate grid-level flexibility for Distribution System Operators — balancing supply, demand, and congestion across the network.
Solar Irradiance Agents
Forecast and manage solar energy production with AI agents that optimize output and coordinate with other assets on the grid.
EV Charging Agents
Smart scheduling and load balancing for EV charging stations — minimizing peak demand while maximizing driver satisfaction.
Why MBML?
How our platform compares to traditional energy management systems
| Feature | Traditional EMS | MBML Platform |
|---|---|---|
| Decision Making | Static rule-based schedules | Autonomous AI agents responding to real-time signals |
| Market Integration | Manual bidding, single market | Automated multi-market participation (SPOT, aFRR, mFRR, FCR) |
| Multi-site Coordination | Isolated per-building optimization | Cross-building flexibility trading in local energy loops |
| Learning | Fixed parameters, manual tuning | Continuous learning from market outcomes and device behavior |
| Flexibility Products | Basic demand response | Granular flexibility pricing (€/kWh up/down signals) |
| Asset Interoperability | Vendor-locked, single protocol | Protocol-agnostic (Modbus, OCPP, BACnet, SunSpec, MQTT) |
| User Control | Opaque, centralized decisions | Transparent agent behavior with safety guardrails |
Decision Making
Static rule-based schedules
Autonomous AI agents responding to real-time signals
Market Integration
Manual bidding, single market
Automated multi-market participation (SPOT, aFRR, mFRR, FCR)
Multi-site Coordination
Isolated per-building optimization
Cross-building flexibility trading in local energy loops
Learning
Fixed parameters, manual tuning
Continuous learning from market outcomes and device behavior
Flexibility Products
Basic demand response
Granular flexibility pricing (€/kWh up/down signals)
Asset Interoperability
Vendor-locked, single protocol
Protocol-agnostic (Modbus, OCPP, BACnet, SunSpec, MQTT)
User Control
Opaque, centralized decisions
Transparent agent behavior with safety guardrails
Meet the Founders
The people building the infrastructure for local energy markets
Ready to unlock local energy value?
Join the energy companies, building operators, and developers already exploring MBML.