Commerce ML Lab

Pioneering research in AI and ML to elevate customer shopping experiences across touchpoints

Our Objectives

To create AI and ML-based product recommendation engines

To facilitate enhanced customer personalization in retail

To help brands enrich their customer interactions and drive conversions across touchpoints

To explore the use of AI algorithms in intelligent, data-driven marketing and customer segmentation

Who can benefit from our research?


Enhancing the experience of retail shoppers by providing personalized product recommendations, tailored content, and a user-centric interface across retail touchpoints


Retailers looking to use AI and intelligent data analysis to streamline operations like targeted marketing and inventory management

Exploring the use of elaborate AI and ML pipelines to ingest and analyze product catalog data from a brand and generate intelligent feature, popularity, and look-based recommendations

Similar products recommendation

An ML stack/pipeline that ingests and analyses catalog data to create highly accurate similar product recommendations for shoppers. These recommendations are delivered to web pages using APIs

Bought-together product recommendation

An AI pipeline that leverages historical purchase data and generative AI to identify products frequently bought together. These combinations/suggestions are then pushed to shoppers on their cart page on e-commerce websites

Trending products recommendation

An AI pipeline that assigns each product a ‘popularity score’ based on a pre-programmed algorithm. Data about the highest scoring products is relayed back to webpages using APIs


Try the latest innovations for yourself

Coming Soon

Collaborate with us

We are open to research collaborations to further enhance our solutionsContribute to or utilize our open-source projects

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