Case Study

A regional cross selling platform uses Aicadium’s recommendation engine to drive hyper-personalised user experience


A prominent regional cross selling platform wanted to improve user experience, increase new seller retention and drive more revenue through transactions. They needed to harness voluminous unstructured data in order to improve the relevance and quality of content and listings, creating a personalised experience for users.


The selling platform uses a product recommendation engine to enhance search quality and enable a hyper personalised experience on the platform. Built by Aicadium and deployed into the marketplaceā€™s search infrastructure using Bedrock, the ML models are designed to be flexible enough to optimise for different business priorities.


The engine generates a significant increase in user click through rates by tailoring fast personalised offerings unique to each customer, through real-time serving of recommendations (sub-30ms) for a seamless user experience. Deployed in weeks, not months, impact on business priorities has happened quickly.

“Bedrock accelerates the time taken to deploy machine learning models, and reduce debt for data science teams.”

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