In 2025, Sensgreen redefined the relationship between building data and action. We evolved from a monitoring tool into an Operational Decision Layer, the intelligence sitting on top of your existing infrastructure.
For us, AI is not a generic dashboard.
It is the engine that turns BMS, IAQ, and Energy data into (1) diagnosis, (2) prioritized actions, and (3) control-ready recommendations.
Here is how we productized AI-driven building operations in 2025.
Sensgreen AI Pipeline
To deliver real value to facility teams, we established a clear three-stage workflow that makes building intelligence tangible:
Connect: We connect to BMS points (BACnet/Modbus/KNX), wireless sensors (LoRaWAN, Sigfox, NB-IoT), and existing meters. Typical inputs include temperatures, equipment status, fan speed, valve/damper positions, and plant signals, alongside sensor-based IAQ and energy parameters.
Understand: We make sense of the data. We spot unusual behavior, explain what is driving it, and detect “fighting loops” where systems (like simultaneous heating and cooling) work against each other.
Act: We turn insights into clear actions for the team. This includes recommended setpoints and schedules, real-time alarms, and ready-to-share reports that make operations consistent across sites.
Concrete BMS Analytics: Beyond the Surface
We moved deep into the equipment that drives building OPEX, analyzing cross-system behavior:
Plant & AHU Optimization: Evaluating chiller staged operation and AHU ventilation adequacy against occupancy-aware demand.
Logic Validation: Detecting setpoint drift, thermostat inconsistencies, and out-of-hours operation in FCUs.
Fault Detection (FDD): Moving from noise-heavy alarms to root-cause diagnosis of sensor drift, stuck dampers, and abnormal cycling.
2025 Impact: Real-World Use Cases
1. Operational Governance: Dubai Residential Portfolio
Deployed across 80 residential buildings, this project established a new benchmark for portfolio-level visibility.
Use Case: We integrated IAQ intelligence and HVAC benchmarking to ensure compliance across a massive multi-site footprint.
AI Outcome: We helped the team move from reactive maintenance to clear priorities across the portfolio, knowing exactly which building needs attention first.
2. Performance & ROI: Philippines Smart AC Control
A large-scale deployment showing how AI-driven optimization translates into measurable operational savings.
Use Case: Improving schedules and setpoints based on real occupancy and performance outliers.
AI Outcome: We helped the team reduce wasted AC runtime, resulting in a 12% energy reduction and around 6,000 fewer runtime hours per month, with fewer manual checks needed from the team.
Explicit Value for Facility Teams
Our AI-driven approach is designed to solve the three biggest pain points in FM:
- Clear priorities: See which building or system needs attention first.
- Clear reasons: Understand what is causing the issue, not just that an alarm happened.
- Less manual work: Get recommended next steps so problems are solved faster.
AI in 2026: Faster Execution
In 2026, we are focusing on making building improvements happen faster:
Automation you can trust: Turning recommendations into tested control logic to build automation in seconds.
Portfolio-wide improvements: Applying proven fixes across many buildings simultaneously, not one by one.
ESG made easy: Automatic reporting backed by real operational evidence.
Thank you for trusting Sensgreen to lead the future of your building operations.
