Onboarding the railway passenger with the help of AI and Big Data

'Railyatri' chugs ahead as travellers embrace predictive features

The typical rail traveller always dreads the festive season. Most Indians have gone through the uncertainty surrounding the confirmation of a booking in the waiting list. The problem becomes a lot more acute during the festival season that starts in October every year. The dilemma of whether to cancel the wait-listed ticket and opt for 'tatkal' or to stick to the current booking in the hope of getting a confirmed berth seems perennial in the Indian context.

Given the massive scale of Indian Railways with multiple trains running through a single route, being overwhelmed seems like an understatement. This is what three techies -- Manish Rathi, Kapil Raizada and Sachin Saxena – are trying to solve through their travel startup, Railyatri, which they co-founded in 2011.

The idea of starting a company in the travel space sprung up when all three were working with an outsourced software product development company, GlobalLogic. “We found that you have MakeMyTrip for flight ticket bookings, redBus for bus ticket, but there are none to solve the problems of train travellers,” said Raizada, an IIT Kanpur and IIM Bangalore alumnus, who also had a stint at mobile VAS firm OnMobile. 
 

The three quit their respective jobs in 2010 with the intention of starting a company focusing on the railway segment, though Railyatri was actually founded only in 2012. In the initial months, they also helped the Centre for Railway Information System (CRIS) to develop the digital interface for the Railways.

Rathi, a Masters in Computer Science from Western Michigan University with more than 20 years of experience in working with companies like Bell Labs, July Systems, and GlobalLogic, is now heading the startup as CEO. The third co-founder, Sachin Saxena, an IIT Kharagpur graduate and MBA from Stanford, who still continues as a board member in Railyatri, is now working with US-based Blue Coat Systems as VP for Cloud Growth and Engagement.

"It wasn't easy to build a startup at the age of 41-42. But, we were excited about solving this problem which affects most Indians," said Kapil Raizada. “Also, funding at the initial stage from an US-based investment firm made our decisions easier."

The Noida-based startup’s app enables a passenger to gauge the probability of getting a confirmed berth in multiple trains. Backed by historical data and analytics, it deciphers for the traveller, which train to opt for, which train usually runs late, the probability of getting a confirmed berth in case a booking is wait-listed, among others. 

Currently, the Railyatri app offers live status of a train and gives the user some idea of the chances of a confirmation. It also provides forecasts on seat availability and enables a traveller to book buses or outstation cabs, and even order food during the journey. Earlier this year, the company introduced a new feature in its app called 'rush-o-meter' that predicts how fast confirmed tickets for every train get sold out. With the help of deep data analysis, a passenger can find out when or within how many hours or days, he should book the ticket in order to get a confirmed seat in a long-distance train.

The startup has historical data spanning four years since its app went live in 2014, and hopes to increase its accuracy as the app generates more data points for analysis and drawing inferences. "We use analytics and artificial intelligence to provide forecasts on various aspects, starting from the timing of a train to chances of ticket confirmation," Raizada said.

Given the opportunity in this space and the complexity of the problem Railyatri is trying to solve, investors have pumped in around $15 million in the company so far. The startup counts marquee investors such as Omidyar Network, Nandan Nilekani, Blume Ventures and Helion Venture Partners as its backers.

As the rail mode of travelling is most widely used by travellers from tier-II and III cities, Railyatri is finding an increasing number of users in semi-urban and rural areas. "Almost 50 per cent of our users are from non-metro cities. Starting from Assam, Arunachal Pradesh to Jammu & Kashmir, our user base is growing faster in the hinterland," said Raizada. “Our next mission will be to scale up the business further as the concept is proven now. In the months to come, we are looking at deepening our engagement with other ecosystem partners, such as hospitality and food delivery chains, to expedite growth.”