Read live BMS data
myCoreAI reads selected data points from the existing BMS, including indoor temperatures, equipment status, energy meters, current setpoints, schedules and relevant system information.
How myCoreAI HVAC optimisation works
EcoAI delivers myCoreAI as an optimisation layer for existing buildings. It reads live BMS data, predicts how the building will respond, and adjusts selected HVAC setpoints such as supply temperatures, pressures, flow-related settings and other agreed control variables, depending on the building and BMS configuration, every 15 minutes within agreed comfort and operational limits.
The existing BMS remains in place and continues to control the building. myCoreAI works alongside it to reduce wasted heating, cooling and ventilation energy without rip-and-replace, major disruption or reliance on manual tuning.
Optimisation cycle
Every 15 minutes, myCoreAI follows the same loop
The process is continuous, controlled and designed to work with the building systems already in place.
Predict building response
Using a digital model of the building, weather forecasts and learned operating patterns, myCoreAI predicts how the building is likely to respond over the coming hours and days.
Optimise within agreed limits
The platform calculates an improved setpoint strategy designed to reduce HVAC energy use while staying inside agreed comfort, operational and plant constraints.
Write setpoints back to the BMS
Optimised setpoints are sent back to the existing BMS. The BMS remains the control system and continues to run the building's own control loops.
Learn and adapt over time
As new data arrives, myCoreAI continues to learn how the building behaves, helping optimisation adapt as weather, occupancy and system performance change.
Existing BMS integration
An optimisation layer, not a replacement BMS
EcoAI is designed to support existing estates, FM and controls teams. The system works with the BMS infrastructure already installed, rather than replacing it. If optimisation is paused or disconnected, the building simply continues operating under the existing BMS logic.
Works with existing controls
myCoreAI reads from and writes selected setpoints to the existing BMS. The building's own controllers, safety functions and local control logic remain in place.
No rip-and-replace
The aim is to improve performance using existing infrastructure, avoiding major plant replacement, disruptive refurbishment or wholesale control system change.
Clear operating boundaries
Optimisation is constrained by agreed comfort bands, occupied hours, operational priorities and plant limitations. EcoAI does not optimise outside the agreed rules for the building.
Designed to support engineering teams
The system does not replace skilled engineers, FM teams or BMS contractors. It provides a continuous optimisation layer that helps maintain good performance between manual interventions.
Savings are delivered within agreed comfort limits
Reducing energy use only matters if the building continues to operate properly. EcoAI configures myCoreAI around the client's comfort requirements, occupancy patterns, schedules and operational needs.
Comfort limits
Agreed comfort limits define the indoor conditions the optimisation must stay within.
Operational schedules
Operational schedules make sure the building is ready when spaces are occupied.
Plant limitations
Plant limitations and site priorities are respected during optimisation.
Normal oversight
The BMS remains in place, allowing normal control, intervention and operational oversight.
The building is learned before optimisation is fully active
Before full optimisation, myCoreAI uses building data to understand how the site behaves. This includes how indoor conditions respond to weather, occupancy, plant operation and existing control strategies.
This learning process helps EcoAI configure the optimisation around the real behaviour of the building, rather than relying on generic assumptions.
Automatic optimisation with clear performance reporting
Although optimisation is automatic, performance remains visible. EcoAI provides reporting that helps estates, sustainability, finance and leadership teams understand the impact.
Baseline comparison
Savings are assessed against a reference energy profile so performance can be compared before and after optimisation.
Energy and carbon reporting
Reporting can show energy, cost and operational carbon reductions in a format suitable for internal stakeholders and governance discussions.
Comfort visibility
Comfort conditions remain visible so savings can be reviewed alongside the building's operational performance.
Evidence for decision-makers
Structured reporting helps support estates planning, sustainability targets, budget discussions and wider portfolio rollout decisions.
Example: university or commercial building with existing BMS-controlled HVAC
Consider a university research building or commercial office with an existing BMS controlling air handling units, heating circuits, cooling plant and occupied-hour schedules.
EcoAI connects myCoreAI to the existing BMS and maps key building data points, such as internal temperatures, outside air temperature, AHU status, heating and cooling setpoints, energy meters and plant operating signals.
During the learning period, myCoreAI builds a digital model of how the building responds to changing weather, occupancy patterns and system behaviour.
Once optimisation is active, myCoreAI adjusts selected HVAC setpoints every 15 minutes within agreed comfort and operational limits. It may reduce unnecessary early-morning plant load, smooth heating or cooling demand, avoid over-conditioning spaces, or adjust supply temperature strategies based on predicted conditions.
The existing BMS continues to control the plant. myCoreAI does not replace the control system. It provides an optimisation layer that helps the BMS run more efficiently.
Result: Reduced HVAC energy consumption, maintained comfort conditions and clearer performance reporting, using the building infrastructure already in place.
Useful terms explained simply
BMS / BAS
The existing Building Management System or Building Automation System already controlling the building.
Building data point
A live data stream from the building, such as a temperature sensor, energy meter, equipment status point or current setpoint.
Setpoint
A value used by the BMS to control building systems, such as a heating or cooling supply temperature.
Digital twin
A model of the specific building, trained using sensor history and operating data.
Optimisation horizon
The forward-looking period used when calculating the best operating strategy.
Baseline
The reference energy profile used to compare actual performance and estimate savings.
Comfort limits
The agreed indoor condition limits that myCoreAI must stay within.
Verified savings using existing infrastructure
At the University of St Andrews, EcoAI delivered verified HVAC savings by optimising alongside the existing BMS, without replacing infrastructure or disrupting normal operations.
21.1%
HVAC electricity reduction
9.2%
Heat reduction
158 MWh
Measured energy saving
29.3 tCO₂e
Operational carbon reduction
Find out what your building could save
EcoAI can review your building or portfolio data and identify where myCoreAI optimisation is likely to deliver the strongest operational and financial impact.