Energy & Oil/Gas · Predictive equipment maintenance
Planned maintenance misses the failures that matter most
Banao builds sensor-to-decision predictive maintenance systems that watch rotating equipment, valves, and compressors in real time — flagging anomalies before they become failures and surfacing them to the right engineer before the next shift.
The system ingests vibration, temperature, pressure, and operational data from your existing sensors, builds asset-level failure models, and prioritises work orders by predicted time-to-failure — not by fixed calendar intervals.
The first call is free · 45 minutes · no obligation
What we build
What Banao's predictive maintenance deployment includes
Predictive maintenance is not a dashboard. It is a trained model per asset class, a reliable data pipeline, and a maintenance workflow that acts on what the model surfaces.
Asset-level failure models per equipment class
We train a separate model for each asset class — pumps, compressors, heat exchangers, motors — on your historical sensor data and maintenance records, not on a generic benchmark dataset.
Multi-signal ingestion from existing instrumentation
The system ingests vibration, temperature, pressure, current draw, and flow data from your installed sensors via SCADA, OSIsoft PI, or direct OPC-UA — no sensor rip-and-replace required.
Time-to-failure ranking and work-order prioritisation
Instead of a health score, the system outputs a ranked list of assets by estimated time to failure with a confidence band — so maintenance teams prioritise by actual risk, not calendar interval.
CMMS integration for the crew that acts on it
Alerts and work orders push directly into SAP PM, IBM Maximo, or your existing CMMS so maintenance teams act in the system they already use — not in a separate AI portal.
Explainability per alert
Each flag shows which sensor pattern triggered it and how that pattern compares to historical failures on the same asset class. Engineers see the evidence, not just the recommendation.
Continuous model retraining on production data
Each confirmed or rejected alert feeds the retraining cycle. The model improves on your fleet data over time rather than decaying as operating conditions change.
Receipts
Where this is in use
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Predictive failure models built on refinery rotating equipment
Banao built sensor-ingestion pipelines and asset-level failure models for rotating equipment at an Indian Oil facility, integrating predicted time-to-failure rankings into the existing maintenance workflow.
Dogfooding
We run our own AI in production before we ship it
Banao operates a ~300-person engineering company on its own AI products. InterviewGod screens every engineering hire; Vikaas drives our own demand generation pipeline. A system that has to survive our daily operation arrives at your plant already hardened.
We do not describe production AI from the outside. The standard we hold a predictive-maintenance model to is the same standard we hold our own production systems to — it has to earn its keep every shift.
Screens Banao's own engineering hires before every offer.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When predictive maintenance AI is not the right answer yet
We scope this honestly. Predictive maintenance fails in predictable ways:
- Thin failure history: if an asset class has fewer than a handful of recorded failures, the model has nothing reliable to learn from. The Discovery Sprint establishes whether augmentation or staged data collection is feasible — or whether the honest answer is not yet.
- Inconsistent sensor data: missing readings, calibration drift, and channel swaps corrupt a model faster than any algorithm can compensate. We audit data quality before we quote a build.
- No maintenance workflow change: a ranked alert list that does not connect to the CMMS the crew uses will be ignored. If process change is not on the table, the model delivers nothing.
How we start
How we start — evidence before commitment
We do not quote a predictive-maintenance programme off a specification sheet. We look at your actual sensor data and failure history first.
- 01
AI Discovery Sprint
2 weeks · fixed price
We ingest a sample of your historical sensor data and maintenance records, test feasibility on your highest-frequency failure modes, and return a baseline model accuracy estimate, data-quality gap report, and ROI projection — yours to keep. If you proceed, the Sprint cost is credited against the build.
- 02
Build
Full sensor-ingestion pipeline, asset-level failure models per equipment class, CMMS integration, and an engineer-facing alert interface. Explainability and retraining cadence are deliverables, not afterthoughts.
- 03
Production & continuous improvement
Live deployment with feedback loops from accepted and rejected alerts, quarterly model retraining, and a model-health report so you know when accuracy is drifting and why.
FAQ
Frequently asked questions
What sensor data do you need to build a predictive model?
Vibration, temperature, pressure, and current draw are the most predictive signals for rotating equipment. We work with whatever your installed instrumentation captures — data historian exports from OSIsoft PI, SCADA CSV, or direct OPC-UA. The Discovery Sprint maps what you have against what each model needs.
How many historical failures do you need per asset class?
A useful model typically needs at least several dozen confirmed failure events per class, though the threshold depends on signal richness and failure-mode consistency. The Discovery Sprint establishes this before any build commitment so there are no surprises.
Does this integrate with SAP PM or Maximo?
Yes. Work-order creation and alert routing integrate with SAP Plant Maintenance, IBM Maximo, and other major CMMS platforms via their standard APIs. The goal is for maintenance teams to act in the system they already use, not in a separate portal.
How do engineers know why an alert fired?
Each alert includes the sensor pattern that triggered it, which failure mode it maps to, and how that pattern compares to historical failures on the same asset class. Engineers see the evidence — so they can confirm, override, and build trust in the system over time.
What happens when the model gets it wrong?
Engineers can reject any alert and log the reason. Rejections feed the retraining cycle, so the model improves on your fleet data over time rather than repeating the same false positives. We track false-positive and false-negative rates across each retraining cycle and flag deterioration.
Get started
Find out which failure mode costs you most — before it happens again
Bring a sample of your sensor historian and maintenance records. In 45 minutes we will tell you whether predictive maintenance is worth building for your equipment fleet, and what accuracy you could realistically expect.
Book a Discovery Sprint