Pie Taxi App

Key challenges faced during development:

• Real-Time GPS Accuracy — Ensuring precise driver-to-rider location mapping without lag or route errors across varying network conditions.
• Dynamic Fare Calculation — Building an algorithm that could account for distance, traffic density, surge hours, and ride type simultaneously.
• Dual-App Architecture — Developing separate yet seamlessly synced experiences for riders and drivers, each with distinct user flows and permissions.
• Scalable Backend Infrastructure — Designing a system capable of handling concurrent booking requests without performance drops during peak hours.
• Secure Payment Integration — Embedding multiple payment modes (cards, wallets, cash) with end-to-end encryption and instant fare settlement.
• Driver Onboarding & Verification — Creating a structured, document-based driver registration flow with admin-side approval to ensure fleet quality and user safety.

Here's how we built the Piet Taxi App:

• Google Maps & GPS Integration — Implemented Google Maps SDK with real-time polyline rendering to ensure accurate pickup/drop routing and live driver tracking for riders.
• Smart Fare Engine — Developed a custom pricing algorithm factoring in base fare, per-km rate, wait time, and surge multipliers — giving users transparent fare estimates before booking.
• Separate Rider & Driver Apps — Built two dedicated apps with role-based access: the rider app focused on seamless booking, and the driver app on navigation, earnings, and trip management.
• Cloud-Based Scalable Architecture — Deployed on AWS with auto-scaling capabilities, ensuring zero downtime and consistent performance during traffic spikes.
• Multi-Gateway Payment System — Integrated Stripe and local payment gateways with instant wallet credit for cancellations and automated driver payout cycles.

• 3X Increase in Organic App Traffic — Within 90 days of launch, the app recorded a 3X spike in organic installs driven by word-of-mouth and app store optimization.
• 68% Booking Completion Rate — Nearly 7 in 10 users who initiated a ride request successfully completed a booking, reflecting strong UX flow and driver availability.
• 42% Reduction in Ride Cancellations — Accurate ETA display and driver tracking significantly cut rider-initiated cancellations post-booking.