Building Health & Maintenance Engine
An IoT-and-ML platform that ingests sensor telemetry from critical equipment, fuses with work-order history and weather data, produces a Building Health Score per asset and a recommended preventive-maintenance schedule.
- Engagement
- 12–20 week build · ongoing IoT and data ops
- Built for
- Facility teams · Property managers · REIT engineering · Large operators
HVAC, elevator, and boiler failures are expensive to fix on emergency timelines and cheap to prevent on planned timelines. The data exists — sensor telemetry, work-order history, weather — but the team that pulls it together to predict failures hasn't been built.
What this is
A predictive-maintenance and building-operations platform for portfolios that take operating costs seriously. Three layers:
- Sensor and data ingestion. IoT telemetry where available (BACnet, Modbus, Niagara stacks), work-order history from the operator's CMMS, weather feeds for seasonal correlation, energy and water consumption from utility-billing or sub-metering.
- Predictive modeling. Per-equipment-class failure prediction (HVAC, elevators, boilers, pumps), anomaly detection on consumption telemetry, planned-maintenance scheduling optimization.
- Operator surface. Building Health Score per asset, prioritized work-order suggestions, comparable rankings across the portfolio for the facility-team dispatcher.
How it's built
Time-series anomaly detection (statistical baselines plus LSTM-class models where the data volume justifies), per-equipment-class failure prediction models trained on the operator's historical failure-and-maintenance data. CMMS integration (UpKeep, Brightly, MaintainX) where the operator uses one. Sensor-deployment piece coordinated with the operator's facilities team.
What you get
- Sensor-and-data-source integration plan.
- Per-equipment-class predictive models.
- Building Health Score across the portfolio.
- Operator dashboard with prioritized maintenance suggestions.
- Quarterly model refresh as the operator's failure history accumulates.
Engagement is shape, not list.
Length and price are functions of the data and the destination. The shape below is the typical engagement.
- Length
- 12–20 week build · ongoing IoT and data ops
- Lead
- Bogdan
- Cadence
- Async, weekly
- Bar
- Production
Scoped during the discovery call against the actual data and the operation it integrates with.
Principal engineer. Architecture and most code ships through one keyboard.
Written updates between, calls when the decision needs the room.
Async correctness, capacity under burst, observability at every boundary.
Products this composes with.
Same suite, or vertical-specialized versions in another.
What buyers ask about this one.
Most of our buildings don't have IoT sensors. How does that work?
Two paths. Where sensors exist (newer commercial buildings, recently-retrofitted assets), we integrate directly. Where they don't, the engagement includes a sensor-deployment plan — typically a small set of high-value sensors per asset (HVAC system, elevator motor, boiler) rather than blanketing the building. We're upfront about the deploy-and-data-collect window before predictions are useful.
What's the prediction window?
Failure-prediction reliably lands in the 14–60 day window for the major equipment classes. Shorter than 14 days is hard (failure-onset signal is often too late); longer than 60 days has noise from too many intervening variables. The engagement sets expectations on this explicitly.
How does the Building Health Score work?
Composite per-asset score across equipment-health dimensions — HVAC efficiency, water and energy consumption anomalies, predictive-maintenance alerts open, work-order backlog. Comparable across assets in the portfolio. The score is descriptive, not prescriptive — it surfaces the assets needing attention.
What's the typical ROI?
Preventive vs. emergency maintenance costs are typically 3-5× different per equipment-class incident; energy-efficiency improvements from anomaly detection are typically 5-15% on conditioned-square-foot energy spend. The ROI math is per-portfolio; the engagement scopes a baseline measurement and a measurement-after-deployment plan.
Pricing?
Implementation-heavy — scoped against sensor deployment scope and portfolio breadth. Discovery call covers both.
If the deliverable matches the gap, the next step is one call.
We'll scope length and price against your data and the operation it integrates with. No retainer, no fishing.
Bogdan and team · async-first · OP—2026