API plus AI helps speed up last-mile logistics

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Getting products and services from point A to point b is what logistics is all about. Being able to track, manage and plan logistics in a simple programmatic approach is not a trivial task. The holy grail of logistics is simplifying what is called “last-mile logistics,” which is how the end product or service actually gets to the end user.

A pain point for many developers building on-demand applications and services is connecting the logistics piece together, especially the location and mapping capabilities. Billing itself as “the world’s largest community of logistics tech builders,” a startup called HyperTrack is aiming to provide the building-block APIs (application programming interfaces) for logistics so developers can focus on what’s core to their business and not worry about the infrastructure layer.

HyperTrack’s platform provides logistics developers with a simple API to connect and access complex logistics workflows. The platform is also supported by a reinforced machine learning model for artificial intelligence (AI) that helps to optimize logistics.

“HyperTrack is a logistics API. It’s like Twillio, but for logistics,” Kashyap Deorah, founder and CEO of HyperTrack, told VentureBeat. “The reason why the world needs a logistics API is the complexity of the last-mile logistics as the world is going on demand, with people pressing a button and things moving to bring products and services to them.”


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In a bid to expand its technology and go-to-market efforts, HyperTrack announced today that it has raised $25 million in a series A round of funding led by WestBridge Capital and existing investor Nexus Venture Partners. 

HyperTrack fits into the supply chain management marketplace, which is estimated by MarketsandMarkets to generate $28.9 billion in 2022, growing to $45.2 billion by 2027.

HyperTrack’s API tackles complexity of last-mile logistics

In the on-demand economy, last-mile logistics has typically involved developers stitching together multiple systems.

Deorah explained that, for example, in a modern app for a delivery-based service, once an order has been accepted the business needs to do route planning, order assignment for the delivery, and tracking throughout the process.

“One of the key components of any logistics stack is you have a brain in the system which figures out who does what and then the driver receives the dispatch,” Deorah said.

Adding further complexity are the myriad data and location tracking systems an organization uses, as well as the different data access approaches that developers need to use for both Android and iOS mobile operating systems.

“So for doing something as simple as determining how much distance a driver travels, ends up involving stitching up mobile cloud and map technologies,” Deorah said. “That’s the part where we’re saying it should be as simple as writing a SQL query or calling an API.”

How reinforcement learning improves last-mile logistics

AI is an important part of the HyperTrack platform, helping to improve accuracy and optimization for the logistics workflow.

In any fulfillment in the last mile, Deorah said that key attributes include the actual customer fulfillment address, the amount of time it takes to get to the address, and what the most efficient route is to get to the location. That’s one of the places where HyperTrack is making use of AI, by using data from its platform for reinforcement learning.

“With every order we are inferring the right customer address, the service time and the route and feeding that back into better planning and assignment,” Deorah said. “So every order makes the quality of the next order fulfillment better.”

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