Modern buildings generate enormous amounts of operational data, yet much of their energy waste remains invisible.
Sia Jangani, Founder of Yopo Solutions, argues that the problem is not a lack of data. Building management systems are designed to control equipment, but they rarely explain whether that equipment is operating efficiently.
By continuously analyzing building data alongside weather, occupancy, and historical energy consumption, Yopo aims to identify operational inefficiencies that conventional alarms and occasional inspections often miss.
In this interview, Jangani explains how AI can reveal hidden energy waste, how credible savings baselines are established, and why transparency, performance-based pricing, and resident trust are essential to building smarter and more connected communities.
Q1. Why does significant energy waste remain despite BMS data, and what can AI identify that conventional monitoring misses?
The data exists. The problem is that nobody's job is to stare at it.A typical residential tower in Dubai has 300 to 500 BMS data points. Temperature sensors, valve positions, pump statuses, chiller loads. The BMS collects all of it, but it's designed to control equipment, not to analyze whether that equipment is running efficiently.
What happens in practice is that a fresh air handling unit gets commissioned at 100% and stays at 100% for years because nobody questions the default. A heat recovery wheel gets disabled during a maintenance event and never gets turned back on. A pump accumulates thousands of excess run-hours because lead-lag rotation was never configured properly. None of these trigger an alarm. The building stays comfortable. The DEWA bill stays high.
What continuous AI monitoring does is compare how the building is actually running against how it should be running, every minute, adjusted for weather, occupancy and time of day.
A human engineer visiting once a quarter might catch a failed sensor. Software watching 400 points every 60 seconds catches the slow drift, the subtle imbalance, the 1.3 degree delta-T that means your pumps are working four times harder than they need to.
Q2. How do you move from a free estimate to a credible energy baseline, and what evidence is needed before both sides agree the savings are real?
The free estimate on our website gives a ballpark based on building type and size. It's directional, not precise. Its job is to answer one question: is this worth investigating further?The real baseline comes from connecting our hardware, the Yopo Gateway, directly to the building's BMS. We read every data point the system produces, typically every 60 seconds, and feed it into our time-series database. We then build a weather-adjusted regression model using historical DEWA bills and outdoor temperature data. In most Dubai towers, weather explains roughly 80% of energy variation. The remaining 20% is operational, and that's where the savings live.
Before either side commits to a number, we need at least 30 days of continuous monitoring data alongside the DEWA billing history. We present the findings transparently: here is your baseline consumption, here is what we measured, here are the specific faults and inefficiencies we found, and here is what fixing each one is worth in kWh and AED per year. The property manager sees the same numbers we do. If the projected savings aren't credible to both sides, there's no deal, and they keep the report for free.
Our model is performance-based. We only earn when verified savings are delivered. That alignment is what makes the conversation honest from the start.
Q3. Why bring energy intelligence and building access together, and how do you balance value, privacy and trust?
The practical reason is occupancy data.If you know how many people are in a building at any given hour, your HVAC scheduling gets dramatically smarter. Right now, most towers cool lobbies, corridors and common areas on a fixed schedule regardless of whether 10 people are in the building or 300. Face recognition access data can close that gap without requiring any action from residents.
The broader reason is that buildings are not just energy systems or security systems. They're communities. A resident who can walk in hands-free, receive a visitor call on their phone, chat with their neighbours, and know their building is running efficiently and reporting its carbon footprint, that's a fundamentally better experience than juggling three different apps from three different vendors.
On privacy: face recognition data is sensitive and we treat it that way. Reference images are uploaded voluntarily by the resident during onboarding. Recognition happens for the sole purpose of granting entry. We don't sell data, we don't profile behaviour, and residents can delete their biometric data at any time.
Trust in a building platform is earned by being transparent about what you collect and why, and by giving residents control over their own information. If people don't trust the system, they won't use it, and an access system nobody uses is worthless.
Jangani’s perspective highlights a broader shift in how buildings are managed.
The next generation of building technology will not simply collect more data. It will need to translate that data into measurable outcomes, clearly explain how those outcomes are calculated, and give property managers and residents confidence in how the technology operates.
For Yopo Solutions, that means combining continuous monitoring, verified energy savings, and connected building services within a model where value must be demonstrated before revenue is earned.
Ultimately, smarter buildings will depend not only on better technology, but also on transparency, accountability, and trust.
