Manufacturing · Downtime reduction
Every unplanned stoppage was predictable — your sensors just couldn't say when
Banao builds downtime-reduction systems that read the failure signal your maintenance team cannot — vibration drift, temperature creep, current draw anomaly — and surface it as a prioritised work order before the machine stops.
The system runs on sensor data your line already generates, connects to your existing CMMS, and sharpens with every maintenance event. Your maintenance team stays in charge; the AI tells them where to look next.
The first call is free · 45 minutes · no obligation
What we build
What a Banao downtime-reduction deployment includes
Stopping unplanned downtime is not one problem — it is sensor coverage, model quality, CMMS integration, and floor-team adoption. We own all four.
Sensor and time-series anomaly detection
Models trained on vibration, temperature, current draw, and pressure signals to identify the specific deviations that precede your most costly failures — not generic out-of-band alerts.
Failure-mode mapping before training starts
We catalogue your machine types, known failure histories, and sensor coverage before any model is trained. The system hunts the failures that actually stop the line, not the ones that are statistically convenient.
CMMS integration and automatic work-order creation
A prediction sitting in a dashboard nobody opens doesn't prevent downtime. The alert becomes a work-order ticket in your maintenance system, assigned and prioritised before the shift engineer checks their phone.
Lead-time estimation per asset
The model tells you not just that a failure is coming, but how many days of runway you have — so planned replacement replaces emergency callout and parts can be ordered before they're urgent.
Shift and process-mix normalisation
Models that account for product mix, line speed, and operator patterns so alarms fire on genuine anomalies rather than on the normal variation that comes with changing what you're making.
Continuous improvement from every maintenance event
Every intervention — whether the failure was prevented or happened anyway — feeds back into the model. The system becomes more accurate as your maintenance team uses it.
Dogfooding
We run on our own AI before it reaches your line
Banao operates a ~300-person engineering company on the AI products we sell. InterviewGod screens every engineering hire we make. Vikaas runs every outbound demand-generation campaign we run. Neither is optional — they are how we operate.
A system that has to survive our own operation gets hardened in a way a demo environment cannot replicate. When we say a downtime-reduction model is production-ready, we mean the same standard we hold our own critical systems to.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When downtime-reduction AI is not the right answer
Sensor AI is not the answer to every stoppage. We will tell you before you spend on it:
- Sparse failure history: if a machine has stopped once in three years, there is not enough signal to train a reliable model. The Discovery Sprint will establish this before any build is scoped.
- Poor sensor coverage: if assets don't have sensors near the failure point, the first project is instrumentation — and the payback timeline shifts accordingly.
- Process-driven stoppages: if downtime comes from scheduling conflicts, material shortages, or operator decisions rather than equipment failure, a predictive model won't fix the root cause.
- Small asset count: below a certain fleet size, a structured PM programme delivers the same outcome at a fraction of the cost. We will tell you which side of that line you are on.
How we start
How we start — baseline before you build
We do not quote a downtime-reduction system without looking at your actual failure history and sensor data first.
- 01
AI Discovery Sprint
2 weeks · fixed price
We audit your sensor coverage, failure history, and CMMS data. You get a feasibility assessment, a ranked list of addressable failure modes, and a payback model — yours to keep whether you proceed or not. If you build with us, the Sprint fee credits against the project.
- 02
Build
Train anomaly models on your historical sensor data, integrate with your CMMS for automatic work-order routing, and calibrate alert thresholds against your specific failure modes and lead-time requirements.
- 03
Production and continuous learning
Deploy to your maintenance team with a plant dashboard, alert configuration, and change-management support. Every maintenance event feeds back to keep the model current as your equipment and process evolve.
FAQ
Frequently asked questions
What sensor data do you need to get started?
Vibration, temperature, current draw, and pressure are the most informative — but the Discovery Sprint establishes what coverage you already have and which failure modes are addressable with it. Some plants get usable models from SCADA historian data alone.
Does this replace our CMMS or our maintenance team?
No. The system integrates with your existing CMMS and creates work orders in your current workflow. Your maintenance team makes every repair decision — the model tells them which assets to prioritise and how much lead time they have.
How much failure history is needed?
Enough labelled failure events to train against your specific failure modes — typically 12 to 24 months of historian data and maintenance records. The Discovery Sprint assesses whether your history is sufficient or whether staged deployment is the practical path.
How far in advance does the model flag failures?
Lead time depends on the failure type and sensor proximity to the failure point. Bearing failures on well-instrumented assets often surface 3 to 14 days ahead of the event. The Discovery Sprint benchmarks this against your actual failure history before you commit to a build.
What happens when the model raises a false alarm?
False positives are tracked and correctable. Your maintenance team can mark any alert as a false positive; those labels feed back into the model. Precision and recall improve over the first months of operation as corrections accumulate.
Get started
Find out how much of your downtime was preventable
Bring 12 months of maintenance records and a list of your worst stoppages. In 45 minutes we will tell you which failures were predictable, which assets to start with, and what the payback model looks like.
Book a Discovery Sprint