Last-mile delivery is particularly tricky in India due to poor infrastructure, population density, vague or incomplete addresses and complex street layouts.

In India, a peculiar challenge arises with 80% of addresses depending on landmarks up to 1.5 kilometres away. This reliance on landmarks makes geolocation difficult for logistics companies, resulting in an average deviation of approximately 500 metres between the given address and the actual doorstep.

To solve this problem, Gurugram-based logistics company Ecom Express developed a solution powered by language models trained on data from nearly 2 billion parcels delivered by the company since its inception in 2012.

Bulls.ai improve delivery quality by up to 60%

Called Bulls.ai, the solution improves operational accuracy by correcting, standardising, and predicting geo-coordinates for addresses across the length and breadth of India. Not just in metros and Tier-1 cities but also into the hinterlands of Tier-2 cities and beyond where the address quality becomes inferior, according to Manjeet Dahiya, head – machine learning & data sciences, Ecom Express Limited.

“Bulls.ai helps in identifying the correct last-mile delivery centre to deliver the shipment based on the consignee address, reduce misroutes, determine junk addresses and correct incomplete addresses/PIN codes to route the shipment correctly. 

“It geocodes the address consignee’s location on the map, assisting our field executives,” Amit Choudhary, chief product and technology officer at Ecom Express Limited told AIM.

Bulls.ai can significantly improve the delivery quality by up to 60%, increase operational efficiency, and slash logistics costs by as much as 30%.

“It also helps in misroute reduction from 7% to 2%. Resulting in a reduction of 5% in shipments reaching the correct last-mile centre at the first go,” Dahiya added.

So far, the company has opened the API to its customers to validate their user addresses. “We have first started with our existing customers and have showcased Bulls.ai to them in our customer panel.”

What makes Bulls.ai unique

The solution is powered by three models – 354 million, 773 million and 1.5 billion parameters and has been trained on 8.4 billion tokens representing 80 million addresses and geo-coordinate pairs.

Ecom Express has a nationwide presence, spanning all 28 states of the country. It extends its services to over 2,700+ towns across more than 27,000 PIN codes, effectively reaching over 95% of India’s population. Over the years, the company has accumulated a substantial amount of data through its extensive operations.

“The architecture is built in a decoder-only transformer pattern, specifically GPT2. It has been trained from scratch and the dataset of the historic addresses that we have delivered in the past is the key data. The training approach is distributed data parallel,” Choudhary said.

Currently, there are no similar solutions in the market. What makes Bulls.ai unique is the training dataset, according to the company. Moreover, existing LLMs like GPT models or the LLaMA models are not tailored to address this particular challenge and do not have the capability to output the geo-coordinates of an address. 

“This is a domain specific LLM and no such LLM exists. For instance, the domain of GPT4/LLaMA is very different from the domain of address and location data. These models cannot tell the geo-coordinates of addresses. Achieving good results with these models will require fine-tuning with significantly large data, which would effectively be a pre-training,” Dahiya explained.

Choudhary said that his team encountered a few challenges when training the model. “For example, a number of optimizations were needed to improve the training speed and reduce the GPU memory footprint such as 8-bit optimizer, mixed prediction training, and gradient checkpointing. 

“This allowed us to train bigger models and faster. During inference, it is quite challenging to use bigger models for real-time predictions as they could be slow. We used pruning of the models to make these bigger models faster at the time of inference,” he said.

Can LLMs solve other last-mile delivery problems?

LLM can also play a pivotal role in addressing many other last-mile delivery challenges inherent in this crucial phase of the supply chain. LLMs can optimise route planning, enhance delivery scheduling, and streamline communication between drivers and customers.

“Apart from address and location intelligence, we see applications in understanding the descriptions of the goods to identify dangerous goods and not route them through the air,” Choudhary said.

Computer vision models are already being used by logistics and supply chain companies to identify dangerous or defective goods, however, multimodal LLMs could potentially do a much better job.

“Understanding of goods is also necessary to figure out the category of the goods. Fraud detection at consignee and seller is another important aspect from the logistics point of view that can be solved through generative AI,” Choudhary added.