Think of Lily AI as a smart retail stylist for your online store that understands products and shoppers the way a great in‑store associate does, then uses that understanding to improve search, recommendations, and product discovery.
Ecommerce retailers often lose sales because search and recommendation systems don’t understand product attributes the way customers describe them (style, fit, occasion, aesthetics). Lily AI aims to bridge that gap by enriching product data and powering more relevant search and discovery, which lifts conversion and reduces abandonment.
Domain-specific product ontology and attribute taxonomy for ecommerce/fashion, plus accumulated labeled product data and retailer integrations that are hard to replicate quickly.
Hybrid
Vector Search
Medium (Integration logic)
Inference latency and indexing cost for large product catalogs with frequent updates.
Early Majority
Compared to generic search/recommendation engines, Lily AI focuses heavily on detailed, human-like product attribute enrichment (especially in fashion and lifestyle), enabling more nuanced merchandising and shopper intent matching rather than just keyword or basic behavioral signals.