Real Estate & Construction · Facility maintenance prediction
Your HVAC failure arrives on a Friday night, at the highest possible cost
Banao builds predictive maintenance models for commercial and residential property portfolios — reading sensor signals from HVAC units, elevators, water pumps, and fire systems to flag likely failures weeks before they happen, not hours after.
The model connects to your existing BMS and CMMS data. Maintenance teams get ranked work orders, not raw alarms. You schedule the engineer at your rate, not the emergency contractor's.
The first call is free · 45 minutes · no obligation
What we build
What a Banao predictive maintenance deployment covers
Prediction without integration is a dashboard nobody opens. We wire the model into the systems your teams already use.
Multi-asset failure prediction
HVAC compressors, chiller units, elevator motors, water pumps, and fire suppression systems — each modelled on its own failure pattern using your historical sensor and maintenance-record data.
Sensor ingestion and anomaly detection
We ingest temperature, vibration, pressure, current draw, and runtime signals from your BMS or direct IoT feeds. Anomaly patterns that precede failure are learned from your history, not from a generic benchmark.
Ranked work-order output into your CMMS
Maintenance teams receive prioritised work orders — sorted by failure probability, remaining useful life estimate, and repair cost vs. consequence — pushed into the CMMS workflow they already follow.
Portfolio-level asset health view
Facility and asset managers see health scores across every building and system type in one view, with drill-down to individual assets and their sensor traces.
Maintenance schedule optimisation
The model recommends when to bring forward or defer planned maintenance based on actual asset condition, so scheduled visits do real work instead of checking on healthy equipment.
Retrofit onto existing BMS and CMMS
We integrate with Siemens Desigo, Honeywell EBI, IBM Maximo, SAP PM, and legacy systems via API or database connector. Your current infrastructure stays in place.
Receipts
Where this pattern is already running
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Predictive HVAC maintenance across a mixed-use portfolio
Reactive maintenance was consuming engineering budget at emergency rates across a portfolio of office and retail buildings. Banao trained a failure prediction model on two years of BMS sensor history and pushed ranked work orders into the existing CMMS. Planned interventions replaced emergency callouts for the systems the model covers.
Dogfooding
We run our own systems on the same principle
Banao operates a ~300-person engineering company on its own AI products. InterviewGod predicts candidate quality before we spend on interviews; Vikaas predicts demand pipeline before we commit sales headcount. The same logic — act on a leading signal, not the lagging one — applies to a failing chiller as it does to a failing hire decision.
We are not describing production AI from the outside. Every model we deploy for a client has to meet the standard we hold ourselves to: does it change a real operational decision, or does it just produce another report?
Screens Banao's own engineering hires — leading signal, not lagging.
Runs Banao's own demand-gen pipeline before headcount is committed.
The honest version
When predictive maintenance is the wrong investment
Not every building portfolio is ready for failure prediction. We will tell you before you spend:
- Thin sensor history: if your BMS logs fewer than 12 months of usable data per asset type, the model trains on too little real failure signal. The Discovery Sprint will establish whether augmentation or staged data collection closes the gap.
- Small fleet: below roughly 50 monitored assets of the same class, a fixed maintenance schedule often outperforms a model. Prediction pays when the fleet is large enough to justify the statistical signal.
- Systems with no run-to-failure history: if an asset class has never failed on your estate, there is nothing for the model to learn failure from. Anomaly detection is still possible, but lead-time accuracy is lower — we will say so explicitly.
How we start
How we start — prove signal before you build the model
We don't quote a predictive maintenance platform off a specification. We look at your actual sensor data first.
- 01
AI Discovery Sprint
2 weeks · fixed price
We audit your BMS exports and maintenance records, test whether usable failure signal exists in your data, and hand back a feasibility report and ROI model — yours to keep regardless of next steps. If you proceed, the Sprint fee is credited against the build.
- 02
Build
Train per-asset-class failure models on your data, build the integration into your BMS and CMMS, and set alert thresholds your maintenance team will actually act on.
- 03
Production & continuous improvement
Live ranked work orders, a portfolio health dashboard, and a feedback loop that updates models as new failure events and maintenance records come in.
FAQ
Frequently asked questions
Which building systems can you model?
HVAC compressors and AHUs, chiller and cooling tower units, elevator motors and drive systems, water and sewage pumps, electrical distribution, and fire suppression systems. Coverage depends on sensor availability and failure history volume — the Discovery Sprint maps this for your specific estate.
Do we need to install new sensors?
Usually not for the first phase. Most modern BMS installations log temperature, pressure, current draw, and runtime data we can work with directly. Where critical sensor coverage is missing, we scope the hardware addition as part of the Discovery Sprint rather than assuming it.
How far in advance can failures be detected?
For compressor and motor failures with sufficient history, the model typically surfaces a degradation signal days to weeks before failure. For less common or poorly instrumented asset classes, lead times are shorter — we publish asset-class lead time estimates at the end of Discovery, not before we have seen your data.
How does it integrate with our existing CMMS and BMS?
We integrate with major CMMS platforms (IBM Maximo, SAP PM, Planon, Archibus) and BMS environments (Siemens, Honeywell, Johnson Controls) via API or direct database connector. Integration is part of the build deliverable — your teams do not manage it separately.
How do we stop the model generating too many false alerts?
Alert thresholds are tuned during build using your historic false-positive rate as a ceiling. Maintenance teams can dismiss and annotate alerts, and those annotations feed back into threshold logic. Over the first three months, false-positive rate drops as the model learns your specific estate's normal variation.
Get started
Bring your BMS export — we'll tell you if the signal is there
In 45 minutes, we can assess whether your existing sensor data contains usable failure signal and what lead times are realistic for your asset types. No commitment required beyond the conversation.
Book a Discovery Sprint