I have been working with NeoITO for over 3 years now. They played an important role in ideating and building our platforms. They are some of the best developers I ever worked with and I enjoyed working with them. I wish I had you guys here as my own team. Thank you for everything you have done.
Brady JohnsonCTO at National Taxi Services
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.
02the 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
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.
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.
- 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