Your building has a BMS.
It doesn't have an energy brain.
A commercial building spends $8,000–50,000 a month on electricity — HVAC is 40–60% of it. There is no energy function. The facilities manager maintains equipment. The property manager manages leases. The sustainability manager writes reports from data she has to request manually.
The BMS was configured by someone who left years ago. It runs fixed schedules that don't match how the building is actually used. Floor 12 is too hot. Floor 8 is over-cooled with nobody there. The NABERS rating is slipping and nobody can explain why.
The building has a nervous system (the BMS). It doesn't have a brain.
The BMS runs schedules from 2018.
The building changed. The schedules didn't.
Your BMS is doing exactly what it was told — five years ago. Schedules haven't been updated since the tenants changed. Setpoints are conservative because nobody wants a comfort complaint. After-hours baseload creeps up because someone added a schedule override that was never removed. HVAC runs 20–30% more than it needs to.
For offices:
The BMS doesn't know Floor 12 has 200 people and Floor 8 has 20. It conditions both identically. Meeting rooms cooled to 22°C with nobody in them. Entire floors ventilated at full capacity on a Friday when half the staff works from home.
For mixed-use and malls:
The BMS doesn't know Tenant A closes at 6pm and Tenant B stays open until 10pm. It conditions the entire common area uniformly when the west wing is empty after 7pm.
The NABERS rating is 4 stars. It should be 4.5. That half-star affects lease negotiations, tenant retention, and regulatory compliance.
40–60%
HVAC share of energy
The single biggest controllable cost — and it follows occupancy, not a clock
20–30%
Typical HVAC overrun
From outdated schedules, conservative setpoints, after-hours creep
0.5 star
NABERS gap costs rent
Government tenants require minimum ratings. Half a star = lost leases
$0
Data to justify decisions
Nobody can prove whether the chiller needs replacing or just better scheduling
Energy follows who's in the building.
Your BMS doesn't know who's in the building.
Occupancy-Driven Intelligence
An office building's energy signature mirrors its occupancy. Monday morning: all floors ramp up. Friday afternoon: half the building empties early. The floor with the law firm works late. The floor with the tech startup is half-empty on Wednesdays. Conference Level 6 spikes at 10am and 2pm then flatlines.
The BMS sees none of this. It runs the same schedule every weekday.
Wattif reads the occupancy pattern from the energy signature itself — no expensive sensor networks required. A floor drawing 40% less plug load than usual? It's half-empty. The pattern is already in the data. Nobody's been reading it.
Tenant-Driven Intelligence
A mixed-use building's energy follows its tenant mix. The ground-floor retailer opens at 9am and closes at 9pm. The office floors above run 8am–6pm. The gym on Level 2 peaks at 6am and 6pm. The food court drives 60% of weekend foot traffic.
Wattif maps tenant hours, footfall zones, and operational patterns. It knows that at 8pm, only the food court and cinema are active. It conditions accordingly.
Floor-by-Floor Occupancy → Energy Allocation
Floor 20
Full occupancy
Floor 15
High occupancy
Floor 8
Half-empty
Floor 3
Near-empty
The BMS conditions all four floors identically. Wattif allocates energy proportional to who's actually there.
Condition to occupancy. Respond to tenants.
Recover after-hours.
Occupancy-driven optimization
For offices
Wattif layers intelligence on top of your BMS. Setpoints widen by 1–2°C in unoccupied zones. Ventilation drops to minimum code requirement. On a Friday when half the building is remote, HVAC on every half-empty floor drops. Total building load drops 15–25%.
Tenant-driven optimization
For mixed-use / malls
Maps each tenant's operating hours and foot traffic patterns. At 8pm, office floors empty, food court winding down, only cinema and gym active. Conditions accordingly. Escalators slow. Parking ventilation drops.
After-hours recovery
Both
Where the biggest savings hide. Most commercial buildings use 30–40% as much energy at night as during the day — far more than they should. Identifies every after-hours load: the AHU that never turns off, the lighting override on floor 7 active since March, the chilled water loop circulating all night for three people.
NABERS / Green Mark intelligence
Tracks NABERS-equivalent EUI continuously — every day, not once a year. Shows trailing 12-month number and where it's heading. Actionable intelligence, not quarterly reports.
15–25% lower HVAC energy.
Better comfort. Higher rating.
The waste isn't from over-conditioning occupied spaces — it's from conditioning unoccupied spaces. Eliminating that waste doesn't affect anyone's comfort. Tenant satisfaction goes up. Energy cost goes down. NABERS rating improves.
15–25%
HVAC energy reduction
From occupancy response, tenant-aware scheduling, after-hours recovery
0.5–1.0
NABERS star improvement
Typical within 12 months from intelligence alone — no capital upgrades
$2–8/m²/yr
Energy cost reduction
Direct opex improvement, visible in every quarterly report
Fewer
Comfort complaints
Because the right zones get the right conditioning — not uniform mediocrity
Connect to your BMS.
See results in a week.
Integrates with existing BMS — BACnet, Modbus, cloud-connected. CT clamps add circuit-level metering where submetering doesn't exist. No BMS replacement. No major capex. Typical 20-floor tower: 2–3 days installation.
01
BMS integration + sensors
2–3 days
02
Quick wins identified
Week 1
03
Baselines built
2 weeks
04
Optimization active
Month 1
05
NABERS impact
Quarter 1
One building gets smarter.
A portfolio gets a competitive advantage.
For property managers with 5–20 buildings: every building adds to the benchmarking dataset. Which building is performing? Which is drifting? Where is after-hours waste worst?
NABERS ratings across the portfolio become one continuous view — not scattered spreadsheets updated once a year. Capex decisions get data. Replace the chiller or fix the scheduling? The answer is in the baseline comparison.
Cross-building benchmarking reveals which sites need attention first
Portfolio-wide NABERS tracking in one continuous view
Capex decisions backed by operational data, not vendor estimates
Same platform, same sensor kit, same logic — building after building
“We discovered that 40% of our after-hours energy was from three AHUs that had never been properly scheduled. Wattif identified it in the first week. We recovered $2,800 per month just by fixing the schedules — before we even turned on optimization.”
Facilities Manager, The GEAR — Commercial office, Singapore
Show us one building. We'll show you what it's wasting.
Floor plans, BMS type, metering setup, NABERS target. We'll connect, build the energy model, and show you first opportunities — usually within a week.