Skip links


Enabling an e-bike platform to predict demand

UI/UX Design

Data Engg & Analytics

Mobile App

Mobyride is an Indian dockless bicycle and e-scooter sharing service. It aims to promote sustainable and eco-friendly transportation by providing easy access to bikes and e- scooters through a mobile app.


Bicycle Retailer

Company size

<100+ Employee

What we did


Mobyride is a company looking at solving the micro-mobility problem in India. Public transport only gets you to hubs, but Mobyride smart e-bikes and e-scooters gets you to your destination in a cheap and convenient way. The company is trying to expand their markets and make their operations much more efficient with the help of AI. A lot of data has been collected through their connected scooters and their apps. The company looks to use this data to make smarter decisions on improving operational efficiency, expansion strategies and customer experience.

Real-time visibility of their fleet along with statistics such as charge percentage, last charged, battery health and GPS information.

Figuring out maintenance windows of the fleet based on commuter usage and trip feedback.

Improving customer experience by having the vehicle available at the right time and the right place for any customer by predicting commuter demand and frequent routes.

Equip support staff with better data to help customer requests


Mobyride is currently collecting a lot of data through their fleet of e-bikes and e-scooters. Also, customer touchpoints such as their apps and website also provide data on customer behaviour. They currently don’t have any way to track the real time data of their fleet. They download the data manually from their devices and batch process this data in their BI tools to gather insights. This leads to a lot of delay in time from insight to action. They want to be able to cater yo their customers demand in realtime. And this is not possible with the current infrastructure they had started with.
Month to gather data

High churn due to unavailability


Real-time Analytics

Synctactic’s platform supports streaming data as an input source and as an emitter as well. In order to get a real-time view of the fleet’s data the the client followed the below steps

Predictive Maintenance

The cold store of the stream was used to understand patterns and train algorithms to predict if a vehicle is due for service or not.

Demand Forecasting

Predicting demand will help improve ROI on the fleet’s operations. This would also mean that the chances of a customer not having a vehicle available in their vicinity is reduced greatly.

Customer Data Access

Synctactic makes it easy to access any data in the system by indexing all metadata whenever a new data source is connected.


The platform hence was used in a variety of ways by the company’s data team to unlock the potential of the data they had been collecting from the fleet and its users. Thanks to the simplicity and ease of Synctactic’s set-up, the team worked on core business problems of the company without worrying about the data infrastructure required to run company- wide data initiatives.

Realtime data availability

Improved team productivity

Recent Work

Launch your product with Arokee

Partner with us for a digital journey that transforms your business ideas into successful, cutting-edge solutions.

This website uses cookies to improve your web experience.