When a customer types “quick dinner tonight,” your store should show pasta sauces and stir-fry kits, not return zero results.
Traditional search fails when customers use natural language, synonyms, or describe what they need rather than exact product names. Our semantic search understands meaning, not just words.
Query expansion via LLM, 4096-dim vector embeddings + BM25 keyword search, a reranking model for precision, and customer popularity scoring. All in milliseconds.
Before searching, the LLM generates synonyms, related terms, and intent variations, so 'headache remedy' also finds ibuprofen, aspirin, and cold compresses.
4096-dim MRL embeddings capture meaning; BM25 keyword scoring catches exact matches. A configurable balance lets you tune the mix per catalog.
After initial retrieval, a dedicated reranker re-scores results for final precision, pushing the most relevant products to the top.
Click and purchase signals feed back into rankings. Products customers actually buy get surfaced higher over time.
Each product gets an LLM-generated description covering functional, emotional, and experiential value, so the search understands outcomes, not just attributes.
Use semantic search without the full assistant. Drop it into your existing search bar via API, boost specific brands, and tune weights in the dashboard.
Real queries, real results. Each example shows what customers type and what the search returns. No keyword matching required.
"something for a quick weeknight dinner"
"healthy snacks for my kids' lunchbox"
"I'm hosting a BBQ this weekend"
"cozy morning drink that isn't coffee"
Upload your product catalog and see semantic search results on your own data. No engineering required for the proof of concept.