Connecting Commuters and Taxis with the Push of a Button

An industry-leading taxi service app that helps travelers get taxis 24/7 without delays or uncertainties

Industry

Transportation

Country

USA

Node Js . Angular

Project Overview

The National Taxi Service was flourishing with new customers and needed to scale its platform quickly to serve the massive inflow of riders without compromising on their app experience.

Our Task

App Redesign

Cloud Migration

Data Processing

BACKSTORY

Rush-hour traffic in the U.S is quite famous, so are their efficient taxi networks that ply all over major cities. The National Taxi Service(NTS) has been a key player in innovating taxi fleet service in the U.S since the early 1980s. RideYellow, a popular ride app from NTS has been operating in 11 states partnering with local taxi fleets. The app is a loved choice for a great many users to conveniently book or call cabs at the tap of a button. But as customers increased tremendously, they realized a crucial problem that required urgent attention: managing daily traffic without any lag whatsoever.

The need for National Taxi Services

An app that caters to users across several states unquestionably requires loads of user data to be processed on a daily basis. With the existing infrastructure, it was tough for RideYellow to manage all the incoming data and cater to all incoming user requests. What’s more, as the incoming data gets uncontrollably immense, the service that could be delivered becomes that much slower. That’s when we were approached to bring in a well-needed technology upgrade for the RideYellow platform.

Their current state of affairs was rooted in three fundamental flaws in the system

Redesign the app to make RideYellow a scalable platform
The app should work in realtime while providing all the features customers love to use in RideYellow
Transform RideYellow into a cloud-based infrastructure with efficient use of data

The team had already built a basic version of the product and had a small group of early adopters. These were seasoned professionals and data scientists who were familiar with their jargon. Our task was now fourfold - create an aesthetic yet simple to use UI, couple it with a highly responsive front end, augment their back end team, and simplify the app experience for customers to quickly create their desired stats from uploaded data

The Challenge

There were 3 real obstacles to be addressed before RideYellow could help a wider range of audiences and be fast at the same time.

Handle large amounts of data without any lag in app performance
Realtime response for user requests, to quickly book, pay, and get alerts on their ride
Reduce overall running costs through better technology adoption

Serving thousands of customers a day requires responsiveness and >99% uptime at any given moment, so designing and implementing the app revamp within the least amount of time became our primary objective.

Testimonials

“RideYellow was previously built on its own infrastructure. We wanted to improve it to provide realtime support for all of its users. Implementing an autoscaling ecosystem and fully migrating to a serverless architecture were the major challenges we saw in improving the app. Our team was able to reshape the app experience to help a wider range of users easily adopt RideYellow for their daily commuting needs.”

Jayakrishnan

Principal Engineer

the result

With a new serverless architecture, RideYellow reported a record 33% lesser operational costs overall
RideYellow is now capable of handling thousands of customers simultaneously with zero delays, thanks to an integrated autoscaling ecosystem to handle data more effectively
RideYellow is now 25% faster, with realtime support to users queries, contributing to its significant rise in user experience
RideYellow is still expanding to other cities and serving thousands of new customers every day