Modelled implementation cases showing the problem, AZIOT architecture, and a realistic performance target. Actual results are established after a baseline audit.
Lighting, ventilation, and heating followed fixed schedules regardless of occupancy and weather.
AZIOT connects meters, occupancy sensors, the heating unit, and lighting; scenarios adjust operating modes to actual demand.
Modelled estimate for a 500-apartment building after 12 months of operation.
Underfloor heating, air conditioning, and radiators competed with each other, requiring constant manual adjustments.
One scenario accounts for temperature, occupancy, open windows, and tariff periods while preventing simultaneous heating and cooling.
Expected effect for a 120 m² apartment over one heating season.
HVAC and lighting ran at the same output during peak and off-peak hours while operators used separate interfaces.
AZIOT combines the BMS, meters, tenant schedules, and footfall data to adjust zone loads automatically.
Modelled annual effect for a 25,000 m² retail facility.
Each location configured thermostats separately; waste and failures became visible only through bills or complaints.
Gateways collect temperature, HVAC modes, and consumption; central rules compare locations and flag anomalies.
Expected first-year effect without replacing primary HVAC equipment.
Video, access-control, and fire events arrived in separate systems, forcing operators to reconstruct incidents manually.
AZIOT correlates events, opens relevant cameras, displays the zone plan, and launches the response playbook.
Modelled reduction in time from alarm to confirmed operator response.
CO₂ exceedances were found through occasional checks, while ventilation depended on staff attention.
Sensors in each room report CO₂, temperature, and humidity; AZIOT controls ventilation and escalates prolonged deviations.
Expected effect after configuring thresholds and weekly room-usage profiles.
Lights ran at full output all night, while failures were discovered only through inspections or complaints.
DALI controllers plus light and motion sensors create an adaptive profile while the platform monitors every line.
Modelled annual effect for 1,000 LED luminaires with overnight dimming.
Trucks followed fixed schedules: some containers were half empty while others overflowed before the next trip.
Fill-level sensors report volume and temperature; AZIOT prioritizes locations and gives dispatchers an optimized stop list.
Expected trip reduction after three months of seasonal data accumulation.
Aggregate bills did not reveal which workshop or line was responsible for excess use or leakage.
AZIOT collects main and submeter data, builds hourly balances, and highlights discrepancies between supply and consumers.
Modelled effect from eliminating persistent leaks and uncontrolled overnight consumption.
Vibration, temperature, and pressure were checked on schedule, so degradation between inspections was found only after failure.
Edge gateways collect telemetry, rules compare trends to the baseline profile, and create a work order before a critical threshold.
Expected downtime reduction after six months of baseline learning for each asset type.
We map the current cost, define the KPI and measurement period, then design the system around the expected payback.
We will review the context and propose the next practical step — without a generic sales presentation.
Thank you. We will review the details and get back to you shortly.
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