Energy is one of the largest operating costs for any enterprise running physical space — and one of the least understood. Most companies know their total electricity bill. Almost none know which floor, which area, or which piece of equipment is responsible for it. When the bill goes up, there is no diagnosis. When a unit malfunctions and draws excess power, there is no early warning. The response is always reactive, and always after the cost has already been incurred.
This is the problem HESEOS was already working on with smart building hardware. What they needed was the intelligence layer that could turn raw sensor data into decisions. That is the part TEN Labs built.
The Challenge
Enterprises running multi-location operations — office campuses spread across a city, coworking spaces with dozens of tenant zones, hotels managing hundreds of rooms across multiple properties — face a compounded version of this problem. The same lack of visibility that exists at the single-building level is multiplied across every location. There is no way to compare consumption across sites. No way to identify which location is efficient and which is wasteful. No benchmarks, no signals, no intelligence.
Facility managers were working from monthly utility invoices and gut feel. That is not a management system. It is a guessing system.
Who This Is Built For
What We Built Together
HESEOS brought the hardware — sensors, panels, and IoT infrastructure capable of reading energy consumption at granular points throughout a building. TEN Labs built the intelligence layer on top: the data pipeline, the analytics engine, the anomaly detection system, and the dashboard infrastructure.
The architecture works in four connected layers:
The Intelligence Layer in Detail
The part that separates this from a simple meter-reading system is the AI engine. Raw consumption data is only useful if it tells you something. Numbers without context are just numbers.
Anomaly detection runs continuously across all sensor feeds, learning the normal consumption pattern for each location, floor, and zone across different times of day and days of week. When consumption deviates from that pattern — a unit left running through the night, a malfunctioning HVAC drawing double its normal load, a floor that wasn't occupied but consumed like it was — an alert fires immediately. Not in the next monthly report. Now.
Efficiency scoring ranks each location and zone on consumption efficiency, benchmarked against comparable spaces and time-adjusted for occupancy. This gives facility teams a clear, ranked list of where to focus optimisation effort — removing the guesswork from where to intervene.
Cost attribution automatically allocates energy costs to the appropriate zone or tenant, which is particularly valuable for coworking operators and hotel groups where accurate cost allocation has traditionally required significant manual reconciliation.
Why This Collaboration Worked
Hardware-software integration is genuinely difficult. Most software companies don't want to own hardware. Most hardware companies struggle to build the intelligence layer that makes their data valuable. This collaboration worked because HESEOS and TEN Labs each brought the piece the other needed.
The result is a system where the hardware is not a product in itself — it is a data collection infrastructure. And the intelligence layer is not a dashboard bolted on top — it is the core value proposition. That integration, designed from the start rather than retrofitted, is what makes the system genuinely useful rather than just technically functional.
For enterprises with multi-location physical operations, this is the difference between a utility bill and an energy intelligence system. And that difference, in operating cost terms, is significant.