Enterprise2021· EMSPL — India

EMSPL Fleet Intelligence

AI Fleet Management for 2,400 Vehicles

A 24-month engagement delivering AI-powered fleet management for 2,400 vehicles — predictive maintenance, GPS intelligence, offline-first apps.

At a glance

2,400

Vehicles managed

real-time telemetry

41%

Fewer breakdowns

in Year 1

12%

Fuel savings

AI route optimisation

23%

Lower maintenance

predictive scheduling

Built with

React NativeFastAPIGPS TelemetryPredictive MLRedisPostgreSQL

Overview

EMSPL operates one of India's largest commercial fleets. They needed to replace a legacy GPS tracker with AI-powered predictive maintenance, driver-behaviour analytics, and fuel optimisation.

The Challenge

The fleet runs across 18 states with patchy connectivity. The platform had to work offline-first on vehicle tablets, sync intelligently, and process real-time telemetry from 2,400 GPS units at once.

How it fits together

Architecture

Vehicle Tablets

background sync

Telemetry Ingest

2,400 streams

Predictive ML

14-day horizon

Fleet Dashboards

live ops view

The Solution

We built an offline-first React Native app with background sync and a FastAPI backend processing 2,400 concurrent telemetry streams. ML models predicted maintenance 14 days ahead from vibration, fuel, and mileage signals, with real-time fleet-manager dashboards.

Results

Breakdown incidents−41%

Maintenance cost−23%

Fuel consumption−12%

The Outcome

12% fuel savings via AI route optimisation, 23% lower maintenance costs through predictive scheduling, and 41% fewer breakdowns in Year 1 — now a 24-month partnership with ongoing development.

2,400 vehicles · 41% fewer breakdowns · 12% fuel savings

Highlights

2,400 vehicles streaming live telemetry across 18 states
Predictive maintenance 14 days ahead of failure
Offline-first tablets that sync when connectivity returns

From brief to production

Delivery timeline

Quarter 1

Telemetry platform

Ingest + live tracking

Quarter 2

Driver apps

Offline-first rollout

Quarter 3

Predictive models

Maintenance forecasting

24 months

Ongoing partnership

Continuous optimisation

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