Lune

Its models analyze real-time behavioral signals, purchase history, and contextual data to surface the most relevant products for each visitor at each moment of their shopping journey.

The platform's recommendation algorithms go beyond collaborative filtering to incorporate product attribute understanding, temporal signals like inventory levels and new arrivals, and merchandising rules that align AI recommendations with business priorities.

This combination of machine learning and business logic produces recommendations that optimize for both conversion and strategic product promotion simultaneously.

E-commerce retailers across fashion, electronics, home goods, and specialty retail use Lune to improve product discovery, increase average order value, and reduce bounce rates from product pages that don't immediately show relevant items.

The platform's personalization depth is most valuable for retailers with large catalogs where the challenge of surfacing the right product from thousands of options is most acute and where generic sorting provides the least satisfying result.