Retail & E-CommerceBuilt By: FalconicLab
Hybrid RAG Search for Natural-Language Product Discovery
Internal R&D combining lexical and semantic vector search so natural-language product queries return relevant results instead of zero-match pages.
~42%
Zero-Result Reduction
<40ms
Search Latency
The Enterprise Challenge
Standard keyword search fails on natural-language queries like "lightweight summer linen suit for a beach wedding" because it can't interpret buyer intent, only exact terms.
The Falconic Engineering Solution
We built a hybrid vector search engine combining BM25 lexical matching with dense embedding search and reciprocal rank fusion, the same architecture detailed in our RAG research post.
Quantifiable Results & ROI
Eliminated zero-result pages for natural-language test queries in internal evaluation
Held search response latency under 40ms in benchmark testing
Architecture designed as a drop-in layer for existing product catalogs
Technologies Deployed
Qdrant Vector DBOpenAI EmbeddingsNext.jsShopify Plus API