About the customer
The customer operates a Class A office, life-sciences lab, distribution center, or hospital footprint with conditioned space served by multiple chillers, air handlers, and terminal units. The portfolio runs on an existing major-vendor BMS with operations led by a facilities lead reporting to the property owner / operator.
Customer name and per-engagement specifics are templated until written attribution is confirmed. The architecture, FDD library, and zero-new-hardware design are unchanged from the live deployment.
The Challenge
Mechanical performance had drifted. Chillers were running outside design IPLV, hot-aisle temperatures crept up between maintenance cycles, and economizer schedules in shoulder seasons defaulted conservative because nobody had time to tune them. The BMS captured all of this in trend logs but the data lived inside the controls console; off-site engineers and the CFO had no way to ask “which chiller is wasting the most kWh today?” without a site visit and a CSV export.
The vendor-led path forward was a full BMS upgrade quoted at six-to-seven figures with a multi-quarter schedule. The customer needed something narrower: keep the existing BMS, layer analytics on top, and target the three or four assets that drove most of the energy waste — not boil the ocean.
The Solution
Intelligent IT deployed AiTBMS as an analytics overlay on the existing BMS. Trend points from the controls server stream into the AiTBMS time-series store on a 1-minute cadence. A library of fault detection and diagnostic (FDD) rules — short-cycling, simultaneous heating-and-cooling, economizer-stuck, valve-leak-by, fan-curve drift — runs continuously over the incoming stream and posts asset-level alerts with cost-of-fault estimates attached. The cost estimates use the customer’s actual utility rate, not a generic $0.12/kWh assumption.
Predictive faulting layers on top: a per-chiller model learns the asset’s normal kW/ton-vs-load curve from its own first 30 days of data, then flags deviation before it becomes a measurable energy event. The dashboard ranks assets by 30-day waste in dollars, so the facilities team always knows which fix to do first. No BMS replacement, no new field hardware — the analytics overlay reads from the existing controls layer.
Results
Outcome metrics over the first 12 months on AiTBMS. Numbers below are templated; live metrics weather-normalize against degree-days before publication.
All energy deltas are weather-normalized against degree-days; raw kWh deltas without DD normalization are excluded from publication.
What’s next
Phase 2 expands the FDD library with customer-specific rules — typically the failure modes the operations team has named informally over years of running these assets but never coded into the BMS. Phase 3 wires AiTBMS alerts into the customer’s CMMS so qualifying faults auto-create work orders with the cost-of-fault estimate already in the ticket body.
- Portfolio expansion to additional sites under the same tenant
- Demand-response: shed-load orchestration during utility peak events
- ENERGY STAR / GRESB / local-ordinance reporting auto-generated from AiTBMS data
- Tenant-billable sub-metering with auto-generated chargeback statements
In their words
“AiTBMS told us Chiller-2 was wasting $4,200 a month. We fixed it in an afternoon. The whole subscription paid for itself in three weeks.”
— Facilities lead, attribution pending written approval
About Intelligent IT
Intelligent IT (a brand of Intelligent Group) builds AiTBMS as the building-systems analytics tier of the AiT product suite. AiTBMS overlays existing BMS deployments — Automated Logic, Siemens, Johnson Controls, Honeywell — with FDD rules, predictive faulting, and dollar-weighted alerting.
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Case study as of 2026-05-06. Customer attribution and live metrics pending written approval. Manuel Ruiz, Founder. © Intelligent Group · intelligentit.io