Problem / Objective:

Swiggy has a mission to bring unparalleled support to the lives of urban customers. To deliver a personalised experience to its customers, the company started to deploy emerging technologies such as AI and ML.

Solution / Approach:

Swiggy uses machine learning to generate a personalised list of restaurants to its consumers based on their past orders, searches and interactions to help customers to find their favourite food. The algorithm makes for faster discovery of restaurants by analysing thousands of data points such as food preferences, a live snapshot of delivery partners in consumers' area, restaurants across price points, and cuisines.

The company generates terabytes of data every week and leverages this data for delivery efficiency, and to connect customers to the right restaurant. After the confirmation of an order, the machine learning takes numerous factors such as restaurant efficiency and traffic conditions into consideration, assigns a delivery partner and finds the best route for him to follow. 

Impact / Implementation:

Swiggy has enhanced its customer engagement and delivery efficiency using AI. On the consumers' end, it is the personalised discovery experience, whereas, on the restaurants' end, it is the use of AI for time-series based demand prediction models that help them plan for high demand periods. The AI models also help in providing a highly accurate delivery promise and enable them to meet that promise efficiently.

In February 2019, Swiggy acqui-hired Kint.io – a startup specialised in deep learning and computer vision for object recognition. The company aims to use the startup's expertise to understand the images more accurately (veg or non-veg) and help in delivering authentic food with the least chances of error to its customers.