Automotive · Predictive vehicle maintenance
A vehicle that fails in the field costs five times more to fix than one flagged three weeks earlier
Banao builds predictive maintenance AI on vehicle telematics, OBD diagnostics, and sensor histories — flagging components that are trending toward failure before the vehicle is in service and the repair window is gone.
The model runs against your fleet data or your dealer DMS feed: no rip-and-replace, no separate telemetry platform. It delivers a ranked list of vehicles and components that need attention, with the signal that drove the flag, so a technician can verify in minutes rather than chase a vague alert.
The first call is free · 45 minutes · no obligation
What we build
What a Banao predictive maintenance system covers
Predictive maintenance is not a single threshold alarm. It is component-level modelling, maintenance scheduling, and the integration that gets the right flag to the right person before a breakdown.
Component-level failure prediction
Models trained on your vehicle sensor histories, OBD fault codes, and mileage patterns that estimate remaining useful life per component — battery, brakes, transmission, and cooling — and flag the ones trending toward failure, not the ones that have already arrived.
Telematics and OBD data pipeline
We ingest telematics streams, OBD-II and CAN bus logs, GPS patterns, and historical service records into a clean feature set the model can actually use — including the missing-data and noise-handling that determines whether a real-world sensor read is meaningful or an artifact.
Maintenance schedule optimisation
Calendar-based intervals are a proxy for condition. Banao replaces them with condition-based scheduling that brings a vehicle in when its component state says so — extending service life of parts that have life left, and catching the ones that do not before a roadside call.
Fleet and dealer alert routing
Predicted failures route to the person who can act: a fleet manager gets a ranked list of vehicles to book in; a dealer service advisor gets the specific component and expected failure window for each VIN opted into telematics. No alert fatigue from undifferentiated warnings.
Warranty leakage pattern detection
Models that run over repair orders and warranty claim codes to find component failure patterns emerging across a production batch or model year — surfacing the cluster of claims that will become a recall weeks before a warranty review would catch it.
Service parts demand forecasting
When you know which components are trending toward failure across a fleet, you can stock the parts before the vehicles arrive. We build the bridge from the predictive model to the parts inventory system so a dealer or fleet depot is not sourcing a part after the vehicle is already on the lift.
Dogfooding
We run our own operation on the AI we build for yours
Banao operates a ~300-person engineering company on its own AI systems — InterviewGod screens every engineering hire we make, and Vikaas runs our own demand-generation pipeline from end to end.
We hold our operational AI to the same standard: it has to work in production before it goes in front of a client. The difference between a vendor who has demoed a model and one who has run it is the difference between a test drive and 40,000 kilometres.
Screens every Banao engineering hire before a recruiter calls.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When predictive maintenance AI does not pay
Predictive maintenance earns its keep when the data and the failure cost support the investment. It does not always — and we would rather tell you in week one:
- Thin telemetry coverage: if a large portion of the fleet has no telematics or OBD connectivity, the model covers a fraction of the vehicles you actually need to manage. A hybrid approach or a phased roll-out is honest; a deployed model that only watches a third of the fleet is not.
- Small fleet size: below a few hundred connected vehicles, statistical patterns are too sparse to build a reliable component-failure model. We will scope whether a generic fleet benchmark is a better starting point for your data volume.
- Very short vehicle lifetimes: if vehicles turn over on short cycles and failure windows are long relative to custody, predictive maintenance does not return enough on the maintenance savings to justify the data infrastructure. We will model the ROI before proposing a build.
How we start
How we start — before any build decision
We examine your actual telematics data, your fleet or DMS structure, and your highest-cost failure modes before recommending anything.
- 01
AI Discovery Sprint
2 weeks · fixed price
We audit your telematics feed or DMS data, profile your failure history against sensor patterns, and identify which components have enough signal to model reliably. You receive a feasibility assessment, a component-priority list, and a full ROI model — yours to keep whether or not you proceed. If you go ahead, the Sprint cost credits against the build.
- 02
Build
Data pipeline, feature engineering, component-level model training, and integration with your fleet management system, DMS, or parts inventory. The data infrastructure and alert routing are deliverables, not prerequisites you must solve separately.
- 03
Production and continuous learning
Deployed model with operator feedback loops, a fleet health dashboard, and alert routing to the right teams. Technician corrections and new failure records feed back into the model on each refresh cycle — accuracy improves with the breadth of your fleet's history.
FAQ
Frequently asked questions
What vehicle data do you need to start?
The Discovery Sprint establishes exactly this. Useful inputs include telematics streams, OBD-II or CAN bus logs, historical repair orders, and mileage records. Where connectivity is partial, we model what is available and quantify the coverage gap — so you know the model's reach before committing to a build.
How far in advance does the system flag a failure?
Lead time depends on the component and the signal quality. Battery and cooling-system failures typically have signals weeks before failure; brake and tyre wear can be detected across service cycles. The Discovery Sprint benchmarks expected lead time on your actual data — we do not quote a lead time off a generic spec sheet.
Does it work with older vehicles that lack telematics?
Partially. Where vehicles have no telematics, the model works on historical service records and mileage patterns — a weaker signal than live sensor data. For mixed fleets, we build a tiered model: connected vehicles get component-level prediction; unconnected vehicles get statistical risk scoring from the service history. We scope the coverage split in week one.
How does a predicted failure reach the dealer or fleet manager?
Alerts route to your existing tools: a fleet management system, a DMS work-queue, an email digest, or an API your dispatcher already reads. We build the alert routing and the integration as part of the deliverable — the right person gets the right flag without an extra app in their workflow.
What does the Discovery Sprint cost, and what do we get?
The Sprint is fixed-price and runs two weeks. You receive a telematics-feasibility assessment, a ranked component-priority list, expected failure lead times benchmarked on your data, and a full ROI model — yours to keep whether or not you proceed. If you continue to build, the Sprint cost credits against the engagement.
Get started
Bring your worst breakdown pattern to our engineers
Show us your telematics feed and your highest-cost failure type. In 45 minutes we will tell you whether predictive maintenance AI is worth building on your data, and what it would take.
Book a Discovery Sprint