Customizable, Performant Mapping APIs Enable Cost-Effective GTM for India’s First Super App

About the Company

An ambitious Indian startup — the country’s first super app — was preparing to go to market with the ride-hailing phase of their operation.

As with any super app, location technology would be at its core. Being in its early stages, there were a number of gaps to be filled and use cases to be supported. At the time, the company was using Google Maps as its service provider. However, this was not without its issues, and it was time to identify a new provider. This would be a decision with significant and far-reaching impacts for the company.

The Problem

Google Maps proved to be an expensive location tech provider, and more importantly, a rigid one. Google’s tools could not offer the level of customization and flexibility that the client desired.

With their go-to-market date getting delayed, the client had to quickly find a cost-effective alternative to Google Maps. One that would be capable of operating at the same level of performance and scale while being customizable to meet the company’s unique requirements. Their wishlist included:

• On-premise solution deployment
• High-quality support for their engineers
• Deep solution customizability
• Leading distance matrix capabilities
• Provider trustworthiness and credibility

Apart from finding a location tech service provider that could fulfill all the above criteria, the client’s young team of engineers also needed some help with various tasks like implementing geofences and designing a night mode.

Our Solution:

After conducting their market research and a few test runs, the client was confident that they had found what they were looking for in our offerings. Aside from checking every box in their wishlist, there were two main reasons for this. First, NextBillion.ai’s was the only solution on the market to offer deep customizability without sacrificing cost effectiveness. Second, we were able to do this while matching the performance levels of Google Maps.

The client would utilize the following NextBillion.ai products in their ride-hailing operation:

1. Distance Matrix API – The Distance Matrix API generates distance and travel time predictions between multiple origins and destinations. It’s a critical component in assigning the right jobs to the right drivers with optimal routes for maximum operational efficiency. NextBillion.ai’s Distance Matrix API supports matrix sizes of up to 5000×5000 elements, lending itself to seamless scalability and operational flexibility.
2. Directions API – The Directions API draws a path and displays clear directions with step-by-step instructions for reaching any given destination. Being a platform-agnostic API, the client team was able to easily integrate it into their existing map stack without any issues or need for infrastructural changes.
3. Maptiles API – The Maptiles API is used to render maps in any desired visual style. From displaying maps in different aesthetic styles (day mode, night mode, etc.) to offering various types of maps (street imagery, satellite imagery, etc.) and even representing custom data visually on map layers (live traffic, terrain information, etc.), this is the API that makes it possible.

NextBillion.ai’s dedicated support desk was in constant contact with the client’s engineering team throughout the testing, integration and implementation processes. Consequently, the client was able to quickly and painlessly resolve whatever few issues did arise during this period.

In addition to the above, our visual artists created certain in-map ride animations for the client, and designed a night mode as per their requirements.

The Outcome

By partnering with NextBillion.ai, the client was able to solve their key challenges of enabling customizability and attaining cost efficiency. NextBillion.ai’s suite of APIs, coupled with a dedicated technical support team, helped the client become self-reliant and develop a truly unique user experience for its customers, which wasn’t possible with Google’s platform.

Key Takeaways:

• Reported a massive drop in costs when they switched from Google Maps
• Gained access to a dedicated support ecosystem throughout the ‘evaluation to implementation’ journey
• Leveraged solutions for unique custom requirements, beginning with in-map ride animations