2023

RideOS – Fleet Management & Rider Safety Platform

A platform empowering both individual riders and fleet operators with real-time safety, ride analytics, and crash detection—designed for India's two-wheeler ecosystem.

Mobility
Operations
Optimization

Case Study Overview

A comprehensive breakdown of the product management approach, from problem discovery to measurable outcomes, following proven PM frameworks.

Problem Discovery

India's two-wheeler ecosystem suffers from a lack of connected safety and operations tools. For fleet operators, there's minimal visibility into how their riders behave on the road. For individual riders, especially in logistics or bike taxi roles, most apps focus only on delivery—not on safety, performance, or real-time assistance.

    Through interviews with:
  • Gig workers (Rapido, Ola, Dunzo)
  • Touring riders and
  • Fleet coordinators at delivery startups and bike rental firms

We identified a common gap: riders needed a trustworthy, always-on ride assistant, while fleets needed lightweight telemetry and safety enforcement—without expensive telematics hardware.

Business Alignment

RideOS was designed to bridge the gap between individual rider empowerment and fleet-wide safety visibility.

    The goals were:
  • Enable riders to track and improve their own riding behavior, with automated safety scoring
  • Give fleet operators access to crash reports, ride summaries, and behavioral trends
  • Build toward an IoT-ready architecture that could later integrate with connected helmets or RideShield hardware
  • Support multi-language, low-connectivity environments typical in Tier 2+ cities

RideOS was both a consumer product and a platform enabler—laying the foundation for subscription-based safety services or partner APIs.

Solution Exploration

After user journey mapping and behavioral segmentation, I led prioritization of the following core features in the MVP:

  • SOS System: Manual and crash-triggered emergency alerts with countdown safety trigger
  • Crash Detection: Built using accelerometer-based thresholds with auto-alert fallback
  • Ride Metrics Dashboard: Distance, speed, duration, and ride logs
  • Safety Score Gauge: Based on ride behavior, enabling gamified feedback and coaching
  • Weather Intelligence: Integrated OpenWeather API for ride planning in real-world conditions
  • Ride Summary Report: Post-ride insights including optional voice notes or incident logging
  • Multi-language UI & Offline Support: Especially for gig riders with intermittent connectivity

Each feature was scoped to be fully functional without backend dependence, while also being modular for future cloud sync via Supabase.

Execution

As Product Manager, I led end-to-end strategy and development execution:

  • Defined the modular architecture using Expo Router + React Native with web compatibility
  • Scoped context providers (RideContext, SafetyContext, UserContext) to cleanly separate user state, telemetry, and app behavior
  • Designed the ride tracking system to be offline-first, storing sessions locally with summary stats
  • Worked with engineers to debug crash detection thresholds and simulate real-world test cases
  • Collaborated with UI/UX to ensure that each component—ride chart, safety score, weather—was understandable by both novice and pro riders
  • Initiated pilots with bike tourers and logistics riders for real-world feedback
  • Created internal dashboards for future use by fleet managers

Outcomes & Impact

Outcomes:
  • Achieved consistent 90%+ crash-detection accuracy during simulation runs
  • Over 70% of early users reported using the ride summary feature daily
  • SOS countdown feature was perceived as "invisible but critical"—users liked the control it gave without accidental activation
  • Successfully laid groundwork for a B2B SaaS model, where fleets can plug into ride-level data or configure safety rules
Reflections:
  • For safety apps, non-intrusiveness and control matter more than pushy automation
  • Segmenting based on rider intent (performance, protection, compliance) helped shape features that scale across use cases
  • Designing for offline-first allowed us to move faster, validate sooner, and reduce backend complexity
  • Gamified metrics (like Safety Score) proved useful not just for riders—but also as a proxy for fleet policy enforcement

Project Artifacts

Supporting materials, frameworks, and deliverables created during the product development process.

Fleet Analytics Dashboard

image

Real-time fleet performance and utilization dashboard

Optimization Framework

document

Fleet optimization methodology and implementation guide

These artifacts demonstrate the systematic approach to product development, from initial discovery through execution and measurement.