Gaurav Bubna, is the Co-Founder of NextBillion.ai a mapping platform built for enterprises. He has previously worked with companies such as Grab, Ola, and ZLemma (acquired by Hired.com)
What initially attracted you to computer science and machine learning?
Even when I was in school, I was always drawn to mathematics and whatever tiny bits of programming I could do then. Thus when I went to college, studying computer science seemed a natural extension. Machine Learning has been more of an “acquired taste” over the years. I loved the combination of practical and real world aspects such as big data and ability to apply it to a variety of real world applications, as well as the theoretical aspects such as probability theory, which I have always found fascinating.
Prior to founding nextbillion.ai you were a Founding member of the Maps product team at Grab, where you helped to scale the team from a 10-member cross functional team to 300+ in a span of ~2.5 years. What were some of the key lessons that you took away from this experience?
A key lesson I learned was that while each country Grab operated in was all part of “South East Asia”, but in practice, they were all so different. Solutions that actually worked well were built in a way that they could be tailored to each business, each country, and sometimes, even different cities within the same country. Most products aren’t built this way, and it’s very hard to balance ability to scale rapidly, but still built that are tailored to each use case. I think this balance is a key lesson I learned from my time there.
Could you share the genesis story behind NextBillion.ai?
We invested in mapping solutions that were specifically needed by Grab but not something that consumer centric maps like Google were built to support. Over time, we were able to drive massive impact for Grab both in terms of unit economics for Grab, as well driving strong competitive differentiation. And while Grab was in a fortunate position to be able to invest so much in their maps, most other companies won’t be able to do so (because they haven’t raised so much capital). So we saw an opportunity to take some of our learning, and build a platform for enterprises globally.
NextBillion.ai is the world’s first-ever decentralized and customizable mapping platform for enterprises. What are some of the benefits for enterprises to use a decentralization platform versus popular options such as Google Maps?
For transport, logistics, and e-commerce companies, we enable better unit economics, lower cost per delivery, and better asset utilization. Given the ‘one-size-fits-all’ nature of options such as Google Maps, these business gains are simply not possible. For other B2B software companies, they are often not able to serve many customer needs because of these one size fits all limitations. We unlock more use cases enabling more revenue and growth opportunities.
Google Maps has limits on the number of APIs that can be used, what are the limits with NextBillion.ai in comparison?
This is a great example of one of our differentiators. We offer extremely flexible commercial models to our customers, including UNLIMITED API calls packages, support for up to 20x higher throughput, and 5x lower latency than best available alternatives.
How does the system use AI to monitor supply demand prediction at a locality level?
Our APIs are used to enable better and more efficient dispatch, more precise pricing changes including surge pricing, which in turn has a direct impact on the supply demand imbalance at very micro levels such as localities.
For ride hailing services could you discuss how the AI can predict traffic to provide better fare and earnings consistency?
Drivers on these platforms look at time as money. Each hour they spend driving, they want some assurance that they will make $X at the end of it. In practice, due to inaccurate distance time predictions from mapping APIs, there is massive fluctuation in the amount of money a driver makes per unit time spent, across different routes, hours of the day, and days of the week. Our AI takes into account past driving behavior, traffic patterns in the city, and suggests extremely precise routes and traffic. This enables extremely accurate pricing for our customers, and in turn, much more predictable earnings for their drivers.
What are some other everyday use cases that NextBillion.ai enables for enterprises?
We enable a wide range of delivery and e-commerce scenarios such as food delivery, groceries, and e-commerce deliveries. We also enable emergency response services such as ambulances to have faster arrival times, police forces to enable efficient patrolling for reduced crime rates, and efficient waste pickups. We also power other behind the scenes use cases that make some of these everyday use cases possible – e.g. trucking routes that bring your e-commerce orders to your nearest fulfilment hub.
Is there anything else that you would like to share about NextBillion.ai?
We believe most enterprises still don’t recognize that maps isn’t just a cool tech thing, but that it can drive 100s of millions of dollars in business impact from the same supply or asset base. Part of what we hope to achieve as a business is advancing the state of the industry itself. If in the coming years, we can help a lot more enterprises recognize the value of spatial data, even if they don’t directly work with us, we would consider our mission partly fulfilled.
Thank you for the great interview, readers who wish to learn more should visit NextBillion.ai.
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