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?
Shoppers
Enhancing the experience of retail shoppers by providing personalized product recommendations, tailored content, and a user-centric interface across retail touchpoints
Retailers
Retailers looking to use AI and intelligent data analysis to streamline operations like targeted marketing and inventory management
Focus areas & current projects
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
Semantic search is a capability built with generative AI and embeddings to power contextual search engines e.g. product search engines on ecommerce sites. It improves search results by incorporating semantic, contextual, and relevance measures with the provided search query at scale
Semantic search capabilities can be integrated with websites, chatbots, or any other touchpoints where users search for products or documents
Product Discovery
Implementation of semantic search on e-commerce websites for faster, more accurate product discovery. Products most relevant to a user query are identified based on textual-product meta attributes and visual-product image parameters
Document Discovery
Implementation of semantic search for document discovery where large language models are used to identify and reference text documents like FAQs, release docs, and more
Playground
Try the latest innovations for yourself
Collaborate with us
We are open to research collaborations to further enhance our solutionsContribute to or utilize our open-source projects
Work with us
Want to push the boundaries of tech innovation with us? Join our team!
