AI for E-commerce

Based on our experience, the top three by ROI: personalized product recommendations (15-30% increase in average order value), smart search with intent understanding (20-40% increase in search conversion), and automated product description generation (5-10x reduction in content costs). All three solutions pay for themselves within 2-4 months.

For basic recommendations, 10,000 orders and 1,000 active SKUs are sufficient. For more complex models (churn prediction, dynamic pricing), more data is needed. If data is limited, we start with rule-based recommendations and simultaneously collect data for ML models.

Via API. AI models work as separate microservices: the store sends a request (user, context), receives a response (recommendations, personalized prices). No need to rewrite the store — just add calls to AI services at the right points.

AI search understands intent, not just keywords. A query like 'anniversary gift for wife' returns jewelry and perfume, not products with the word 'gift.' We use vector embeddings and semantic search: products are found even without exact word matches. Search conversion grows 20-40% compared to Elasticsearch without ML enrichment.

Visual search lets users upload a photo and find similar products: a user photographs a dress on the street and the store shows catalog matches. We use computer vision models (CLIP, ViT) to create image embeddings and nearest neighbor search. This is especially effective in fashion, furniture, and decor, where describing a product in words is harder than showing it.

We recommend a 'quick wins first' strategy: start with AI recommendations in the 'Frequently bought together' block — this connects in 2-3 weeks via API and immediately increases average order value. Second step — smart search. Third — auto-generation of descriptions for new products. Each step delivers measurable ROI without requiring changes to the store's core code.