Demand and ETA intelligence
Forecast service pressure and arrival ranges from approved operational signals, with uncertainty visible to dispatch teams.

Mobility · Ride-hailing platform
A connected passenger, driver and administration ecosystem for booking and operating urban rides.
AI Development opportunities
A current extension could help operators interpret demand, trip exceptions and support signals faster while protecting riders and drivers from opaque automated decisions.
Forecast service pressure and arrival ranges from approved operational signals, with uncertainty visible to dispatch teams.
Summarize stalled, rejected or unusual trips and route consequential cases to an accountable operator.
Retrieve approved trip and policy context, draft responses and keep every sensitive escalation with a trained person.
Surface payment, account and service anomalies for investigation without automatically penalizing a passenger or driver.
AI features would require precise-location privacy, passenger and driver safety review, fairness testing, explainable matching or pricing signals, measurable evaluation, continuous monitoring and human review for consequential decisions.
Explore AI Development →01
Project context
Queen Car is a ride-hailing product with separate passenger, driver and management systems.
OpenGeeksLab joined the ongoing product to extend existing functionality, support integrations, reshape server architecture and redesign the passenger and driver experiences for iOS and Android.
02
Problem to be solved
03
Software solution
Product analysis, server-side modernization and interface redesign formed interconnected workstreams that improved capacity, stability and usability.



04
Passenger application

Two-factor registration and password recovery through email or phone.
Pickup, destination, scheduling, vehicle class, accessibility, language and driver preferences.
Quick matching, vehicle tracking, route details and timely push notifications.
Card, cash, discounts and the payment options described in the source.
In-app chat and calls for trip coordination.
English and Arabic, light and dark themes, history, ratings, referrals and car sharing.
05
Driver application

Two-factor access and document submission for driver verification.
Search, accept or reject rides with route optimization and push updates.
Daily and monthly ride and earnings history.
English and Arabic interfaces with light and dark themes.
The source describes scratch-card balance replenishment for the driver account.
06
Administration platform
The administration scope described in the source brings operational oversight, configuration and commercial workflows into one management environment.
A real-time dashboard for trips, users and the wider service.
Verification workflows plus driver, vehicle and trip management.
Account management, complaints and deal administration.
Billing, invoicing, referrals and commission management.
Content, push notifications, cities and geolocation boundaries.
A configurable bonus system for operational programs.
Technology
The product uses the mobile, web and distributed backend technologies below.
Java
Kotlin
Swift
Objective-C
NestJS
Angular
MongoDB
Redis
TypeScript
gRPC
RabbitMQ
Express
Mongoose
TwilioDelivery snapshot
The PDF lists two backend developers and one specialist each for frontend, Android, iOS, project management and UI/UX design.
The team included two backend developers and one specialist each for frontend, Android, iOS, project management and UI/UX design.
Project outcome
The ongoing product uses a monetization model combining fees from driver earnings with passenger charges.
The server and integration work improved high-load capacity, fault isolation, scalability, performance and security.
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