The Vision Behind NextBillion.ai: A Conversation with Gaurav Bubna

At NextBillion.ai, our technology is shaped by the operational challenges logistics  businesses face every day. Behind the platform are the people who listen to customers, question existing approaches and decide which on-ground problems to solve next.

In this edition of our People Behind the Platform series, we speak with Gaurav Bubna, Co-Founder about the vision behind NextBillion.ai, the experiences that inspired its creation and how that vision continues to evolve.

We asked Gaurav about the lessons learned from customers, the decisions that shape the product and the opportunities he sees for location intelligence. The conversation also offers a personal perspective on building a company, leading a team and choosing where to go next.

Let’s get started.

What inspired you to build NextBillion.ai, and what problem did you want to solve?

Gaurav: The idea came from our experience working on routing and mapping technology at Grab, Singapore. We saw how closely routing and mapping affected everyday on-ground operations, from helping drivers reach the right location to delivering a reliable customer experience.

We also realised that businesses needed more flexibility than existing solutions offered. Different operations had different requirements, yet adapting to multiple sets of technology to those requirements was often difficult.

That experience led us to launch NextBillion.ai in 2020. We wanted to give logistics businesses greater control over their location technology so they could build around their operational needs while keeping a check over their productivity and growth.

How does that founding vision shape NextBillion.ai today?

Gaurav: The central idea is to make location technology work for the logistics business using it. A route can look efficient on a map and still be impractical for the team expected to execute it.

A delivery fleet might need to account for truck capacity and customer time windows. A field service business might need to match technicians to jobs based on skills and availability. Those requirements should influence the plan from the beginning.

We already work with some of the world’s largest logistics and transportation businesses whose operations depend on getting these details right. A bulk fuel distributor serving some of North America’s most remote regions, uses NextBillion.ai Route Optimization to support deliveries where dependable routing is critical.

Similarly, one of the leading non-emergency medical transportation (NEMT) providers in the US uses our Route Planner to coordinate Medicaid and Medicare rides nationwide, helping transport more than 60,000 patients safely each month.

Our suite of APIs and Route Planner helps businesses incorporate their operating requirements into routing and scheduling, creating plans that better reflect how their teams work in the real world.

How do customer needs influence the direction of the platform?

Gaurav: A customer request is a starting point. The useful conversation is about the problem behind it: what is slowing the team down, which decisions require manual intervention, and what a better outcome would look like.

For example, a request to reduce travel time may also involve balancing workloads or keeping customer commitments. Understanding those priorities helps us define the right solution.

We have applied this approach in our work with a leading on-demand last-mile delivery marketplace, helping it use NextBillion.ai’s Route Optimization to match and route 30,000+ drivers across 50+ U.S. metro markets.

The challenge is deciding which needs require customization for a specific operation and which reflect broader problems we can address through the platform. That balance keeps product development connected to customer needs and practical business outcomes.

Reader’s tip: Get a quick walkthrough of our latest product updates. 

How do you build confidence in markets where your brand or experience is less established?

Gaurav: We work with some of the world’s largest fleet management, field service, logistics and transportation businesses. This experience, combined with our in-house expertise and strong team of location technology specialists, helps us build confidence with prospective customers from the outset. We understand complex operational challenges and can demonstrate how our technology can support their business needs.

Customers should evaluate our relevant track record, regional coverage, implementation approach and support arrangements against their operational requirements. Each market brings different challenges, data availability and business practices, so the evaluation should focus on how effectively we can support their specific use case.

How do you decide whether a customer requirement should become a core product feature or a custom solution?

Gaurav: We start by understanding the business problem behind the request and whether our existing capabilities can address it through configuration.

If the requirement is relevant to multiple customers, aligns with our product direction and can support different operational workflows, it becomes a candidate for the core platform. We also consider its impact on usability, performance and ongoing maintenance.

When a requirement is closely tied to one company’s internal processes, proprietary data or a specific integration, a custom solution may be more appropriate. For example, we have built advanced custom constraints and capabilities for MTM, America’s largest non-emergency medical transportation (NEMT) broker, to address its unique operational challenges. For such work, we define a clear scope, implementation expectations and a plan for maintaining the solution as the platform evolves.

These decisions can change over time. A custom requirement may become a core capability when broader demand emerges and we can adapt it into something reusable.

Also Read: People Behind the Platform: A Conversation with Pushkar Kulkarni

How do you manage the long-term risks of depending on external maps and traffic data providers?

Gaurav: We focus on the dependencies that directly affect customers such as map data quality, service availability and long-term costs.

We evaluate map and traffic data against the regions and workflows our customers operate in. The question is whether coverage, freshness and reliability are sufficient to support dependable routes and ETAs for that particular operation.

We also preserve flexibility to use alternative sources where feasible. Switching providers requires careful validation of data quality, routing results, technical compatibility and licensing. An alternative is useful only if it can support the customer’s requirements reliably.

On the commercial side, we assess licensing conditions, pricing changes and service continuity to understand their impact on future availability and costs. A source that meets today’s technical requirements also needs to remain commercially viable over time.

Our responsibility is to make these dependencies clear to customers: what data supports their operation, where its limitations lie and how a significant provider change could affect their coverage, service or costs.

Which customer challenges is NextBillion.ai best positioned to solve, and how do you decide what falls outside its core focus?

Gaurav: We are best positioned to help businesses whose routing and scheduling decisions must reflect complex operational requirements. Vehicle capacities, customer time windows, workforce skills, shift schedules and road restrictions all need to work together while keeping costs and service commitments in view.

That is why logistics, field services, fleet operations, on-demand services and transportation are central to our focus. Our role is to translate that complexity into practical plans that drivers, technicians and dispatch teams can execute reliably.

Our customer relationships reflect this focus. We work with one of the world’s largest field service platforms, which uses our APIs through an on-premises deployment. These relationships push us to solve increasingly sophisticated problems grounded in real operating needs.

When evaluating a new requirement, we ask whether it strengthens these workflows, addresses a recurring customer challenge and fits our platform’s architecture. Just as importantly, we consider whether we can deliver and support it reliably over time. That helps us decide which requirements should become broader platform capabilities and which need a more specialized approach.

Customer discovery, automotive mapping and specialized navigation require different expertise and product investments. We assess those opportunities on their own merits and remain clear about what we can support today. Our scope should expand where customer needs, our strengths and the ability to deliver lasting value come together.

Looking ahead, what is your vision for NextBillion.ai, and what should customers look forward to over the next few years?

Gaurav: My vision is for NextBillion.ai to become an increasingly dependable part of how businesses plan and execute their operations. That means helping teams manage route complexity, adapt when conditions change and make better use of their vehicles, people and time.

Over the next few years, I want us to deepen our core capabilities while making the platform easier to evaluate, integrate and use. Better regional data coverage, clearer product guidance and a smoother experience for developers and operations teams will be important priorities.

We also want to adopt AI more deeply across our own teams to increase productivity and serve our customers better. I see opportunities to improve how we develop products, share knowledge and respond to customer needs, giving our people more time to solve complex problems and deliver world-class products.

An important part of this vision is Location Reasoning AI, the product line we are building to connect AI applications with our routing, optimization and geospatial capabilities. By giving AI agents access to reliable travel-time, distance and routing calculations, we can help businesses solve more advanced location-intelligence problems. For example, an AI assistant could help identify which technician can reach a customer within a service window or evaluate how to reassign deliveries when operations are disrupted.

Ultimately, I would measure our progress through customer outcomes: shorter planning cycles, fewer manual interventions, more reliable execution and greater confidence in operational decisions. The same principle applies to our own adoption of AI,its value should be visible in how effectively we work and how well we serve our customers.

From Product Vision to Operational Results

Gaurav’s perspective connects the vision behind NextBillion.ai with the everyday realities of its customers.

Clear product priorities, transparency about capabilities and close attention to customer needs shape how that vision becomes useful technology. As the platform evolves, its progress should be measured by what businesses can achieve. Simpler planning, fewer manual interventions, more reliable execution and better use of their resources. For the people behind NextBillion.ai, the work ahead means making each product decision count in the operations that depend on it and helping customers build more efficient and resilient businesses.

This conversation is another highlight of our employee Q&A series. Stay tuned as we introduce more of the people behind NextBillion.ai who are helping make logistics smarter, more efficient and better aligned with real-world operations.

Connect with the NextBillion.ai team  today to explore how smarter routing can help you streamline operations and scale your business.