Built With MongoDB: Milky Way AI

Sagar Setu received his PhD in helicopter flight dynamics and has a fascination with deep learning and integration within the field of aerospace. However, helicopter flight dynamics is not what Sagar is involved in today.

Through Entrepreneur First, an international program that helps entrepreneurs launch companies, Sagar met Eunice Wong, a fellow aspiring entrepreneur, who introduced Sagar to the world of retail, which he calls a “fantastic playground” for any engineer to be involved in. The pair founded Milky Way AI, with Wong as CEO and Sagar as CTO.

Milky Way AI is designed to empower the largest retailers and brands globally with real-time visibility into how their products and their competitors’ products are being merchandised across thousands of stores.

In this edition of #BuiltWithMongoDB, we chat with Sagar about the ways Milky Way AI creates opportunities for retailers, his favorite MongoDB features, and how the competitive AI industry keeps him motivated.

MongoDB: What does the product look like now, and how does it work?

Sagar: Our flagship product is called InstaShelf. It’s a mobile app that we put in the hands of distributors and merchandisers. When there is a person stocking the shelves and we put the app in the hands of that person, they are able to snap a photo, which then goes through our proprietary computer vision engine. This generates a variety of insights that are valuable for both the distributor and the brands. We are looking into how we can share this same data with and make it equally valuable to the retailers.

We developed this product over the last year and launched our beta three months ago. Since then we have gotten quite good traction in terms of users in a number of countries that are deploying the product.

MongoDB: Let's talk about that traction. How far along are you?

Sagar: We started in January with a 15-store pilot for Kelloggs in Singapore. We have deployed across 150 stores now, and we are set to deploy across a few hundred more in Malaysia and the Philippines. By the end of next year, we hope to be in three more countries — just with Kelloggs. The typical number of users for each of our pilots is around 15 to 20 merchandisers visiting between 50 and 60 stores. In a typical audit, the user takes 10 to 15 photographs and our AI identifies what's on the shelf from these pictures, reporting on key metrics such as a brand’s share of shelf compared with a competitor brand, products that are out of stock, product placement compliance, and so forth.

MongoDB: What does your tech stack consist of?

Sagar: The web and mobile components of our solution are built using MongoDB, React Native, React, Node.js, and Python Flask. The computer vision pipeline is built on both TensorFlow and Pytorch. We use MongoDB for all our database requirements — transactional and analytical.

Our top criteria for choosing the tech stack were proven scalability and stability, and a wide developer pool. It was important in the early stages to keep the team lean and the product flexible, and the choice of MongoDB Atlas turned out to be a great one. The support for being schemaless was crucial in allowing us to stay nimble as we learned the nitty-gritty of the domain. With features such as triggers and BI Connector, we could orchestrate various components of the solution right from the Atlas GUI, saving us hundreds of working hours.

MongoDB: What are some of your favorite features in MongoDB?

Sagar: My favorite feature is the support with autoscaling, which is the primary concern if you’re building anything into production. I’ve never had to worry about that. I don’t even think about it; I have just turned the features on, and it allows so much creativity. With MongoDB Atlas, I have peace of mind.

MongoDB: What is something that you are learning right now?

Sagar: Learning is a constant, working in the field of AI. A wonderful plus point is you always have so much competition: there might be a paper tomorrow that basically undoes everything you’ve done — something comes out that is far superior to the method you just took two months to deploy. So I’m always reading, learning, and trying to improve our solutions.

MongoDB: What’s one of your favorite books?

Sagar: The Selfish Gene. It’s not exactly technical, but more on the scientific side. That’s more of my kind of read. I really like the thought process the book instills in you. It gives you an understanding of the world — the good, the bad, and learning not to take things personally.

Looking to build something cool? Get started with the MongoDB for Startups program.