Premium Electric Cab Platform
End-to-end digital experience for India's first premium electric cab service with sustainability metrics and IoT-connected vehicles
Overview
Mahindra launched Glyd as India's first premium electric cab service, targeting environmentally-conscious urban professionals willing to pay more for a sustainable, premium experience. We designed and built the complete digital ecosystem including rider apps, driver apps, fleet management dashboard, and IoT integration with Mahindra's electric vehicles.
The Problem
Electric vehicles in ride-hailing face unique challenges: range anxiety, charging infrastructure, and premium pricing justification. Glyd needed to differentiate from Uber/Ola not just on sustainability but on the entire experience—making customers feel good about choosing electric while delivering premium service quality.
Key Challenges
Range Anxiety Management
Riders worry about whether the cab can complete their trip. Drivers need intelligent routing that accounts for battery levels and charging station locations. The app needed to communicate range confidently without technical jargon.
Premium Experience Differentiation
At 30% higher pricing than competitors, every touchpoint needed to feel premium. This meant better UX, cleaner vehicles, more professional drivers, and tangible sustainability metrics that justified the premium.
Fleet Operations Complexity
Electric fleet management is harder than ICE vehicles. Charging schedules need optimization. Battery health affects range. Vehicle-specific data needed integration with booking systems for accurate estimates.
First-to-Market Pressure
No playbook existed for premium electric ride-hailing in India. Every feature decision required careful thought about market positioning. Speed-to-market was critical before competitors copied the concept.
How We Built It
Experience Design & Prototyping
Extensive user research with target demographic (urban professionals, 25-45, environmentally conscious). Designed end-to-end rider journey emphasizing sustainability at every touchpoint. Created high-fidelity prototypes tested with 100+ potential users before development.
Rider & Driver Apps
Built Flutter apps for cross-platform consistency with premium animations and transitions. Rider app features real-time EV visualization showing battery, range, and carbon savings. Driver app includes intelligent routing with charging station awareness.
IoT & Vehicle Integration
Integrated with Mahindra's vehicle telematics for real-time battery status, location, and health metrics. Built data pipeline processing vehicle telemetry for fleet management insights. Created predictive models for range estimation based on driving patterns and conditions.
Fleet Management Platform
Developed operations dashboard for fleet managers showing vehicle status, driver performance, and booking analytics. Built charging schedule optimizer maximizing vehicle availability while minimizing electricity costs. Implemented driver scoring based on efficiency, safety, and customer ratings.
Solution Highlights
Sustainability Dashboard
Every ride shows carbon savings vs petrol equivalent. Cumulative impact tracking lets riders see their total environmental contribution. Shareable eco-stats for social media.
Premium Booking Experience
Clean, minimal interface with smooth animations. Upfront pricing with no surge. Vehicle arrival with real-time EV visualization. In-ride entertainment controls for a truly premium feel.
Intelligent Range Management
Drivers never assigned trips they can't complete on current charge. Routing factors in charging stops for longer journeys. Battery-aware driver allocation ensures service reliability.
Fleet Operations Platform
Real-time visibility into entire fleet health and location. Charging schedule optimization reducing electricity costs. Predictive maintenance alerts from vehicle telemetry.
Technical Deep Dive
The rider and driver apps use Flutter for code sharing across iOS and Android with native-feeling animations. Vehicle integration uses MQTT for real-time telemetry (location, battery, speed) streaming to AWS IoT Core, processed by Lambda functions and stored in DynamoDB. Range estimation uses a gradient boosting model trained on historical trip data with features including current battery percentage, HVAC usage, traffic conditions, and driver efficiency rating. The charging optimizer uses linear programming to schedule charging sessions across the fleet, minimizing electricity cost (time-of-use pricing) while ensuring sufficient vehicle availability for predicted demand.
AI Capabilities
Range Prediction
ML models estimating remaining range based on driving conditions and patterns
Demand Forecasting
Predicting booking demand by area and time for fleet positioning
Charging Optimization
Scheduling charging to minimize cost while maximizing availability
Driver Efficiency Scoring
Rating drivers on energy-efficient driving behavior
Technology Stack
Mobile
Backend
IoT
Maps & Location
ML/Optimization
Results & Outcomes
95%
Customer satisfaction
Post-ride rating average across all trips
88%
Booking completion
Share of initiated bookings that complete
67%
Repeat riders
Customers who book again within 30 days
12 tons/mo
Carbon saved
Estimated CO2 reduction vs petrol vehicles
First
To market
Premium electric cab service in India
15%
Range improvement
Through intelligent routing vs naive GPS
Services Used for the Client Product
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