AI that reduces building energy costs — without replacing existing systems
HomeMind acts as an intelligence layer on top of existing building automation. It analyzes HVAC, occupancy, weather, energy prices, PV and battery storage to detect losses, recommend improvements and — after validation — autonomously control the building within defined comfort and safety limits.
We do not start by taking control. First, we measure the baseline, detect losses, train the model on data from the specific building and only after validation move to active control.
First, we detect obvious losses. Full AI potential is confirmed against the baseline.
In many buildings, initial savings do not require full AI autonomy yet. They come from correcting wrong setpoints, overheating, overcooling, empty-zone operation, inefficient schedules and lack of response to weather or real room usage.
The full potential of HomeMind appears after collecting data, learning the building behavior, validating decisions in shadow mode and moving to predictive HVAC control. That is why we do not promise one number for every facility — first we measure the baseline, then we show a realistic optimization range.
Buildings have automation, but often lack intelligent control
The biggest losses occur where a system heats, cools or ventilates without understanding real building usage, thermal inertia, weather and the cost of energy.
Static schedules
The building follows hours and setpoints instead of responding to occupancy, weather, thermal inertia and current energy costs.
Energy in empty zones
Rooms are often heated or cooled even when nobody uses them, increasing costs without improving comfort.
No clear decisions
Facility managers see charts, alarms and data, but do not always get a simple answer: what to change, where the losses are and what impact can realistically be achieved.
HomeMind is an AI layer on top of existing building automation
We connect to existing data sources: BMS, Home Assistant, HVAC, sensors, PV, battery storage, tariffs and weather forecasts. Then AI learns how the building responds to control, occupancy and external conditions.
This allows HomeMind to first detect problems, then recommend changes, and after validation actively adjust HVAC and other system setpoints — without replacing the installation.
Heating, cooling, ventilation, heat pumps, air conditioning and zone temperature control.
Temperature, occupancy, usage patterns, preferences and manual user overrides.
Tariffs, demand peaks, PV self-consumption, battery storage and demand-side flexibility.
Detection of overheating, overcooling, controller hunting and empty-zone operation.
HomeMind learns the building without a heavy digital twin
Instead of manually modelling every part of the installation, HomeMind learns directly from operational data: temperatures, setpoints, occupancy, weather, HVAC operation, PV, battery storage and the history of manual corrections.
This can make deployment faster and feasible also in older buildings, where technical documentation is incomplete and existing automation relies on simple schedules or local rules.
How we deploy HomeMind AI in a building
HomeMind does not take control on day one. First, we learn the building, measure the baseline, analyze data and train the model on the real behavior of the installation. Only after validation do we move to active control.
Data analysis and baseline
We connect HomeMind to available infrastructure: BMS, Home Assistant, HVAC, sensors, PV, battery storage or weather data. At this stage, the system does not control the building — it only observes, collects data and creates a baseline for energy use and comfort.
- we check available data points and measurement quality,
- we detect overheating, overcooling, empty zones and wrong setpoints,
- we estimate the real savings potential for the specific facility.
Model learning and recommendations
The model learns how the building responds to outdoor temperature, occupancy, HVAC operation, heat gains, energy tariffs and thermal inertia. Based on this, HomeMind provides concrete recommendations that can be implemented manually or used as preparation for automation.
- AI learns the building’s thermal profile,
- it identifies zones with the highest improvement potential,
- it proposes safe changes to setpoints, schedules and HVAC operating limits.
Shadow mode — AI makes decisions without controlling the system
In shadow mode, HomeMind runs in parallel with the existing system. AI proposes control decisions, but does not execute them yet. This makes it possible to compare current automation with how the HomeMind model would behave.
- we compare AI decisions with current control logic,
- we evaluate the impact on comfort and energy use,
- we validate the model before allowing it to control the real installation.
Active control with manual override
Only after validation does HomeMind start controlling selected HVAC setpoints. Control is performed within agreed comfort and safety limits. The user or administrator can change the temperature at any time, just like before, and AI uses that correction as information about preferences.
- AI controls setpoints in a safe and reversible way,
- users retain the ability to make manual corrections,
- if needed, AI can be disabled and the installation continues to operate as before.
One technology, several deployment models
HomeMind can be deployed as a cloud service, a local edge controller, a Home Assistant integration or software embedded next to the BMS. The right option depends on IT policy, available data and the scope of control.
Cloud
Fast start without onsite hardware. A good option for analyses, pilots, building portfolios and customers who accept cloud operation.
Local / Edge
Data and control decisions can stay inside the building. This option is designed for sites where privacy, security and local operation are priorities.
Home Assistant
A fast retrofit path for homes, small facilities and installations with many ready-made smart home, HVAC, PV and sensor integrations.
BMS / SDK
Integration with an existing BMS, SCADA or building controller. Suitable for technology partners and larger facilities.
Highest potential: buildings with costly HVAC and suboptimal control
HomeMind is designed to scale from a single building to multiple centrally managed facilities. The greatest value appears where HVAC represents a significant cost and current control does not respond to real building usage.
Commercial buildings
Offices, service facilities, retail, hotels and buildings where HVAC represents a significant share of operating costs and facility managers need measurable recommendations.
Campuses and portfolios
Multiple buildings, shared analytics, efficiency benchmarking, standardized control and cost optimization across a portfolio.
Homes and larger residences
Heat pumps, underfloor heating, PV, battery storage, Home Assistant and comfort automation without manual schedule tuning.
HomeMind is not based on one promised number. We measure specific KPIs.
The real impact depends on the building, data quality, HVAC installation, usage profile and the current level of automation. That is why every project starts with a baseline, and the areas below are treated as measurable KPIs to be confirmed during analysis and pilot deployment.
reduction of HVAC energy use compared with baseline and weather conditions.
lower cost through HVAC control, tariffs, PV, battery storage and peak reduction.
time within the target temperature range and the number of overheating or overcooling events.
time spent heating, cooling or ventilating rooms without real usage.
number of user and administrator interventions indicating control mismatch.
ability to temporarily shift or reduce load without sacrificing comfort.
better use of local generation and storage through predictive building operation planning.
more accurate energy use prediction thanks to context: weather, occupancy and control history.
HomeMind should not be a cost. It should finance itself through savings.
The goal of deployment is a situation in which energy savings and lower operating costs are greater than the cost of using HomeMind. That is why we start with potential analysis, not with selling a large implementation project.
Analysis first
We check data, current control, HVAC operation and the largest sources of loss. If a building does not have meaningful savings potential, we do not recommend moving to full deployment.
Fast payback
In many buildings, the first value comes from simple corrections: wrong setpoints, overheating, empty-zone operation and inefficient schedules. This space can often be detected already during the data analysis stage.
Full AI potential
The greatest value appears after the model learns the building behavior and predictive control is enabled. Then HomeMind can optimize HVAC, comfort, tariffs, PV, battery storage and flexibility in one system.
Example ROI calculation
A simple payback model starts with the annual HVAC-related energy cost and a confirmed percentage reduction against the baseline. This lets the customer quickly see whether deployment makes economic sense — before deciding on full AI control.
What the customer gets after deploying HomeMind
Lower energy costs
HVAC optimization, reduced empty-zone operation and better use of building thermal inertia.
Better comfort
Pre-heating and pre-cooling prepare rooms before they are actually used.
Less manual work
AI detects problems and proposes decisions instead of requiring constant schedule tuning.
Flexibility-ready
The system can account for tariffs, PV, battery storage and peak demand reduction.
The potential is real, but the result always depends on the specific building
Industry reports and research on building controls indicate significant savings potential from better sensors, advanced controls and correction of operational faults. HomeMind brings this direction into a practical deployment process: from data analysis, through shadow mode, to measurable control.
That is why we do not start the conversation with a percentage guarantee. We start with the question: what data is available, how the current control works and where the building actually loses energy.
We use what is already in the building
HomeMind is retrofit-ready. We do not start by replacing infrastructure, but by connecting to data and control where it is already available.
HomeMind prepares buildings for energy flexibility
Buildings have the potential to shift energy use over time. HomeMind uses comfort, occupancy forecasts, weather and thermal inertia to manage load without simply reducing user comfort.
Automatic Demand Response
Reduction or shifting of consumption within agreed comfort and safety limits.
Predictive Load Shaping
Predictive load shaping for heat pumps, HVAC and heating systems.
VPP Forecasting
Better forecasts thanks to real building behavior and user context.
Building Coordination
Coordination of multiple buildings in campuses, property portfolios and energy communities.
Frequently asked questions
Does HomeMind take control of the building immediately?
No. HomeMind starts with data analysis, baseline and shadow mode. In shadow mode, AI makes test decisions, but does not execute them in the installation. Only after validation can the model move to active control.
Do we need to replace the BMS or HVAC installation?
Not in a typical scenario. What is needed is access to existing building infrastructure: BMS, HVAC automation, Home Assistant, controllers, sensors or other data sources. HomeMind works as an AI layer on top of the current system.
Can the system run locally?
Yes. HomeMind AI can run locally, also without a permanent internet connection. Local installation provides a high level of privacy and security, because data and control decisions can remain inside the building.
Are savings guaranteed?
We do not promise one number for every building. Potential depends on current control, data quality, HVAC installation, usage profile and energy prices. That is why the first step is baseline analysis and a realistic savings range for the specific facility.
What if AI makes a wrong decision?
HomeMind operates within agreed comfort and safety limits. Control comes down to setting parameters that the user or administrator could previously adjust manually, such as temperature setpoint, schedule or operating mode.
Users can always change the temperature just like before the modernization, and such a correction becomes information for AI about new needs. In case of failure or administrator decision, AI can be disabled and the installation will continue to operate as before.
Write to us about a building analysis
Briefly describe the building type, existing automation, data sources and HVAC system. We will check whether the building is suitable for HomeMind analysis, which stage is best to start with and where the largest savings potential is likely to be found.
After the first contact, we can prepare a short assessment: what data is available, which systems can be analyzed or controlled and whether it makes sense to move to baseline, shadow mode and broader deployment.
HomeMind Sp. z o.o.
E-mail:
mpuchara@gmail.com
Address:
ul. Widokowa 32
32-087 Wola Zachariaszowska
Poland
Company details:
KRS: 0001065573
NIP: 5130289705
REGON: 526754772
