Search that understands what shoppers mean.

Finds what shoppers mean, not just what they typed

Plain keyword search misses "warm waterproof jacket" when the product is tagged "insulated shell." younifyd combines traditional lexical search with vector embeddings and merges the two result sets using Reciprocal Rank Fusion, so a strong match on meaning can surface a result that shares zero keywords with the query.

You don't choose between "good keyword search" and "smart semantic search" — every query gets both, merged into one ranked result set.

2 signals mergedlexical + vector, fused per query

Pin, bury, or hide any result, for any query, without a redeploy

A relevance algorithm is never the whole story — sometimes you need a specific product pinned to the top of "best sellers," or an out-of-season item buried without removing it from the catalogue. younifyd's merchandising rules let a non-engineer make that change directly, scoped to specific queries or globally.

Nothing here requires touching the ranking model itself — rules sit on top of it and apply instantly.

instantmerchandising changes, no redeploy

A typo or a brand's own slang shouldn't dead-end a search

A misspelled query gets a real "did you mean" suggestion instead of a blank results page. Synonym expansion handles the gap between how your catalogue is labeled and how shoppers actually talk about it — "sneakers" finding products tagged "trainers," without you maintaining a thesaurus by hand.

configurablesynonym rules per index

Prove a ranking change works before it ships to everyone

Relevance profiles are configurable and versioned, so you can run two ranking strategies against real traffic and see which one actually converts better — not guess from a demo. Roll the winner out to everyone once you have the data, roll back instantly if it underperforms.

built inranking experiments, no separate tool

Let an AI decide how to search, not just what to search for

Beyond ranking results for a query you give it, agentic search lets an AI plan the search itself — breaking down a vague or multi-part request ("something for a rainy camping trip under $150") into the structured query your index actually needs, using a model you configure.

This is a distinct mode from standard hybrid search, built for exactly the kind of open-ended questions a shopper would ask a human associate, not type into a search box.

AI-plannedquery construction, not just ranking

Search quality improves from what shoppers actually do

Every search, click, and conversion is logged as an event, feeding back into relevance tuning and the recommendations engine alongside it. Search quality here isn't a one-time setup — it's something that keeps improving against real behaviour, not catalogue data alone.

continuousevent-driven relevance feedback