Logistics & Supply Chain · Fleet maintenance prediction
Banao builds fleet maintenance prediction systems that flag vehicle failures before a breakdown strands a load — trained on your telematics, GPS, engine diagnostics, and service history, integrated into the fleet management tools your workshop already uses.
This is not a monitoring dashboard. It is a failure model with a maintenance schedule on the other end: a specific vehicle, a specific component, a specific window before failure probability crosses the threshold your ops team sets.
Indian Oil— predictive maintenance models deployed across a national tanker fleet.
Predicting a failure is useful only if it reaches a maintenance planner in time to act. We build the full chain — model, schedule, and integration.
Models trained on your vehicle sensor data, OBD fault codes, service history, and telematics to flag brake, engine, tyre, and transmission failures before they happen — scored by failure probability and days-to-action, not just raw alert counts.
AI-generated maintenance windows that align predicted failures with vehicle availability, workshop capacity, and parts stock — so vehicles come in before they break, not after a roadside incident burns the SLA.
A data engineering layer that cleans and normalises GPS, CAN bus, fuel, idle, and fault-code streams from mixed-fleet hardware into a single feature store the prediction model can use consistently.
A live view of every vehicle's health score, upcoming maintenance obligations, and active failure alerts — built for ops managers and workshop planners, not data analysts.
Predictions feed directly into your fleet management system, workshop scheduler, or parts ordering workflow — no manual relay between the model output and the team that acts on it.
Hard-braking, over-revving, and excessive-idle patterns that accelerate component wear are surfaced per vehicle and per driver, giving fleet managers a corrective signal before the damage appears in a service bill.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Indian Oil
Indian Oil moves fuel across one of India's largest distribution networks, where a tanker breakdown or a missed depot run costs more than the repair. Banao applies telematics and service-history models to flag vehicle failures before they become roadside incidents, integrated into the existing fleet management and workshop scheduling workflow.
Banao runs a ~300-person engineering company on its own AI products daily. InterviewGod screens every engineering hire before a human interview. Vikaas runs our own demand-generation pipeline from sourcing through to a booked call.
A prediction model that has to survive our internal operations is hardened before it reaches your fleet. We are not describing production AI from the outside — we depend on it every working day, and we hold a fleet maintenance model to the same standard.
Predictive maintenance is one of the most over-sold categories in logistics AI. We will tell you when the conditions aren't right:
We don't quote a predictive maintenance system off a spec sheet. We look at your actual telematics and service data first.
We audit a sample of your telematics, fault codes, and service history, test failure-signal strength on your vehicle types, and hand back a feasibility report and ROI maths — yours to keep whether or not you continue. If you proceed, the Sprint cost is credited against the build.
Data engineering first: telematics ingestion, cleaning, and feature engineering as a deliverable. Then the failure model, maintenance schedule logic, and integration with your fleet management system and workshop tools.
Deployment with workshop-team override, a fleet health dashboard, and maintenance workflow integration. The model retrains on each new service event so accuracy improves as your fleet history grows.
We don't have telematics on all vehicles. Can we still start?
Yes. Mixed-instrumentation fleets are the norm. The Discovery Sprint maps what signal exists per vehicle class and what the model can do with it. Vehicles with richer telematics get more precise predictions; vehicles with only OBD or fault-code data get simpler, still-useful alerts. Retrofitting low-cost GPS and OBD loggers is sometimes part of the recommendation.
What failure types can the model predict?
Failures the telematics can see: engine, brake, tyre wear, transmission, battery, and fuel-system faults are the most common targets. We train to the failure classes that actually ground your vehicles, not a generic list. The Discovery Sprint identifies which ones have enough signal and service history to model reliably.
How does it connect to our existing fleet management system?
Integration with your FMS, workshop scheduler, or ERP is a build deliverable, not a separate project. We have integrated with both enterprise fleet platforms and in-house spreadsheet-driven workflows — the output is a maintenance action in the tool your workshop team already opens, not another dashboard to check.
How long before we recover the build cost?
The Discovery Sprint produces the ROI model — breakdown cost avoided, maintenance labour saved, and parts procurement improvement — so you have the maths before committing the build budget. A typical path is a 2-week Sprint, a 6–8 week build, and a 4-week rollout, with measurable breakdown reduction in the first full quarter after deployment.
Our fleet is mixed — different makes, models, and ages. Does that work?
Mixed fleets are the norm, not the exception. We build per-vehicle-class models where the data supports it, and fall back to make-model-age groupings where it does not. The ingestion pipeline handles the heterogeneous signal formats — that is most of the data-engineering work in the first two weeks.
Bring your last 12 months of service records and whatever telematics you have. In 45 minutes we'll map the failure-prediction opportunity and the ROI maths behind it.
Book a 45-min scoping call
