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About the Company

With a vision to disrupt the trucking industry, an Indian B2B logistics startup was aiming to become a leading B2B logistics marketplace for retailers, manufacturers and truck operators, and also to be one of the first companies to offer on-demand trucking services. 

To effectively become ‘the Uber of on-demand trucking services’, the company was looking for feasible alternatives to Google Maps that would offer improved cost efficiency, better performance in their mobile application and higher accuracy for addresses. 

At the time the company decided to evaluate NextBillion.ai as a potential partner, the client’s logistics network included 100 drivers, operating across 30 delivery trucks and 10 long-haul trucks.

The Problem

Similar to Uber’s functionality for trucks, the client allowed users to quickly book an on-demand truck through the company’s app. The user would be presented with options to specify a particular type of truck, along with the pricing details for the whole trip. 

Since costing would need to be calculated and displayed upfront, a robust and accurate distance matrix API was critical for the company. The client also wanted to offer live location and tracking visibility to customers as soon as the booking was made.

Along with becoming an ‘Uber for trucks’, the company aims to become an ‘AirBnB for warehouses’ and an ‘Amazon marketplace for truckers’, which tie in with the overall objective of becoming the go-to application for trucking operators, truck repairs, identifying storage and warehouse options, and a marketplace for trucking accessories manufacturer. 

This meant that the company also faced a challenge in building a network of easily accessible warehouses and repair stations. Such a network would ensure that all drivers would receive the necessary support to accept and fulfill customer requests for truck deliveries without any hassle, becoming a hub for picking up new requests and finding trucking-related support.  

Apart from warehouses, the client also wanted to provide options for truckers to identify and access the nearest fuel stations, truck repair and accessories shops, etc. For this, the company was on the lookout for geocoding services that guaranteed high accuracy and fast performance.

Our Solution:

To begin with, the company wanted to address the pain points of generating optimized routes and accurate cost estimates upfront for the customer. After checking out other mapping service providers, the client decided to evaluate NextBillion.ai’s large Distance Matrix API and Geocoding API, and compare the costing and performance of NextBilion.ai’s solutions with that of their existing partner.

Upon evaluation of our APIs, the company made the following observations:

  • By leveraging NextBillion.ai’s large Distance Matrix API, the company could now quickly and easily calculate exact prices for transportation costs. By supporting larger matrix sizes (5000*5000 as opposed to the standard 25*25), NextBillion.ai’s Distance Matrix API allowed the company to scale easily without any size limitations, and also factor in additional fleets of trucks and unplanned stops.
  • NextBillion.ai’s Geocoding API offered higher accuracy and faster performance when it came to identifying truck locations and POIs. This was crucial, because with the existing partner’s platform, the company had issues with the level of accuracy — especially in South Indian territory, where most of their operations were based out of.

The Outcome

NextBillion.ai’s large Distance Matrix API and Geocoding APIs are now being used by the company to streamline truck dispatch and response times, identify and navigate to warehouses and repair stops, and track delivery trucks in real time. 

By leveraging two key solutions from NextBillion.ai’s portfolio of mapping technologies, the company is now in a position to solidify their application’s positioning as the ‘go-to place for on-demand trucking services, scale their operations by adding more trucks, expand their network of repair stops and warehouses, and also save on operational costs. 

Key takeaways of the engagement:

  • Added new capability of real-time view of trucks carrying goods once they leave the warehouse 
  • Easy integration into existing APIs
  • Attractive price point; cost-effective compared to competitors

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