Retail & E-Commerce

Our personalisation engine combines collaborative filtering, content-based models, and real-time behavioural signals to serve next-best product recommendations, dynamic homepage layouts, and personalised email campaigns. Across deployments we have achieved a 34% average increase in conversion rate and up to $180M in incremental revenue for a single retail client.

Yes. Our recommendation infrastructure uses distributed vector search (Pinecone, Weaviate) and approximate nearest-neighbour algorithms to deliver sub-100ms recommendations at scale. We have deployments handling 10M+ SKUs and 5M+ concurrent users with 99.99% availability.

Our demand forecasting models − using gradient boosting, temporal CNNs, and Prophet for seasonality − consistently achieve 90–94% accuracy at SKU-level for weekly forecasts. This translates to 20–30% reduction in stockouts and 15–25% reduction in excess inventory for our retail clients.

Yes. We build computer-vision-based virtual try-on (clothing, eyewear, shoes) using 3D body estimation and garment draping models, and visual search (snap-to-shop) that returns similar products from catalogue images. These features are available as mobile-native SDKs or web-based integrations.