AI-Powered Style Recommendation Engine

A fashion retailer sought to improve how customers discovered products on their platform. Rather than relying on manual curation or basic filtering, they needed a system that could surface relevant items based on each shopper's individual behaviour and preferences. We designed and integrated an AI-driven recommendation engine that worked alongside their existing platform with minimal disruption.

We began by working closely with the client to understand the shape of their data — what was available, how it was structured, and where the gaps were. This informed the design of a recommendation system built on proven machine learning models, tuned specifically for fashion-oriented behaviour patterns.

The engine was trained and fine-tuned using the brand’s own data, including product catalogue attributes, historical purchase records, and aggregated customer interaction signals. Key behavioural inputs included browsing sessions, category affinity, and repeat engagement patterns — all used to model what a given customer was likely to find relevant next.

Integration was handled as an extension of the existing platform rather than a replacement of any part of it. The recommendation layer was built to sit alongside the current infrastructure, exposing outputs through an API that the front-end could consume without significant rework.

The recommendation system achieved an accuracy rate in the range of 80–90%, a meaningful result given the breadth of the catalogue and the diversity of customer behaviour. Recommendations became noticeably more relevant, reducing the effort required for customers to find items aligned with their preferences.

Product discovery improved across the platform, with customers engaging more consistently with suggested items. The retailer also saw a positive impact on conversion potential, as better-matched recommendations brought more intent-aligned products into the customer journey at the right moment.