Train Ticket Discovery App ConfirmTkt Raises Angel Funding

Bengaluru-based mobile focused train ticket discovery and prediction platform, ConfirmTkt has raised angel round funding from Pravin Agarwala among other investors. The company aims to utilize the raised funds to improve its technology, hire new talent and to grow as a ticket discovery tool which can show all the possible ways to get a confirmed ticket.

Pravin is an IT industry veteran, and a serial entrepreneur. He headed  the cloud based computing division for SAP as its vice president for several years. He is currently the advisor and director for various technology-led startups besides being the Co-Founder and CEO of Better place safety solutions.

Dinesh Kumar Kotha, Co-Founder, ConfirmTkt said, “In the coming quarters our primary focus would be on mobile as 80% of the user base is on mobile and it is doubling every quarter . Daily our mobile platforms serve 15k active users approximately.”

The funding amount has not been disclosed by the company, but as per RailNews report, it is said to be Rs 75 lakh. The startup uses analytics to predict the train ticket confirmation based on historical trends. These predictions assists a user in deciding whether to book a train or not if the booking is in wait-list and plan a trip accordingly.

Founded in 2014 by Dinesh Kumar Kotha and Sripad Vaidya, ConfirmTkt also contributes to ticket discovery in trains by effective utilization of all the available quotas on a train. The startup has launched apps for Android and Windows phone users. The app currently sees 15,000 visits everyday and around 20,000 new users every month.

In future the company aims to evolve into a last minute ticket discovery tool for all modes of transport including bus and flights. The startup’s current focus is building ticket discovery engine and it will focus on transactions at a later stage.

It competes with Trainman, a startup which collects journey details in advance for each train and builds model based on heuristics. It predicts the likely status of a ticket by using machine learning.

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