How AI and better engineering lift performance
Recommendation and search are the usual first wins. Hybrid search blends keyword matching with embeddings so shoppers can find products by describing them. Recommenders use browsing, basket and catalog signals, with simple rules covering brand-new items that have no history. We measure each change with controlled tests rather than assumptions. Visual search and size recommendation can follow once the basics are working, particularly in fashion and home categories where choosing is hard.
Operational AI can be more valuable than front-end AI. Demand forecasting guides replenishment, pricing models respond to competitors and stock levels, and returns analysis reveals sizing or quality issues. Generative tools also draft product descriptions and answer support questions, with review for brand tone and factual accuracy before publishing. Each of these tools is only as good as its feed, so data quality work comes before model work.
