Exii
Built a segmented, highly scalable recommendation engine for a retail and e-commerce product, delivering over 1,000 distinct product-recommendation algorithms within a few months.
Key Results
Distinct product-recommendation algorithms
Delivery timeframe
Highly scalable methodology
The Challenge
A retail and e-commerce catalogue this varied can't be served well by a single, one-size-fits-all recommendation model. Different product segments behave differently, and building bespoke recommendations for each — at scale and at speed — is a serious engineering challenge.
The Solution
As a Halo AI engineer, we built a segmented, highly scalable recommendation engine using a segmented programming methodology.
Segmented Programming Methodology
A structured approach that allowed bespoke algorithms to be produced rapidly across many product segments.
Highly Scalable Engine
Delivered over 1,000 distinct product-recommendation algorithms within a few months.
The Impact
Tangible Outcomes
Delivered over 1,000 distinct product-recommendation algorithms
Used a segmented programming methodology for scale
Built a highly scalable recommendation engine
Shipped within a few months
Key Takeaway
Used a segmented engineering methodology to deliver recommendation algorithms at a scale and pace that a single-model approach simply couldn't match.
Need Recommendations Across Many Segments?
Let's discuss how a segmented approach can scale your recommendation engine fast.