Why Your Site Search Is Losing Sales, and How AI Rebuilds It Around Intent
Type “something under 30 dollars for sensitive skin” into most ecommerce search bars and watch it fail. Keyword based search only works when a shopper already knows the exact product name, and most shoppers do not think in exact product names.
Why this keeps costing you
Poor site search is one of the most under diagnosed sources of lost ecommerce revenue, because it fails silently — a shopper sees zero results, assumes the product does not exist, and leaves rather than trying a different search. Meanwhile, thin or inconsistent product listings, often inherited from a supplier feed, make even a good search engine underperform because the underlying data is incomplete.
How we build it
Every product is embedded into the same vector index used by the sales assistant, so a natural language query is matched against meaning rather than exact keyword overlap — “under 30 dollars” and “for sensitive skin” resolve as constraints and attributes, not literal string matches. A separate enrichment agent runs against thin listings inherited from supplier feeds, generating structured attributes and clearer descriptions from raw spec sheets so the search index has better data to work with in the first place, rather than trying to compensate for bad input at query time.
What this looks like once it is running
- 1Intent based semantic search instead of exact keyword matching
- 2Automatic attribute and description enrichment for thin or inconsistent listings
- 3Real time inventory sync so search never surfaces a dead end, out of stock result
- 4Natural language filtering, such as “under 30 dollars” or “gift for a runner”
- 5Works alongside the sales assistant agent for one consistent shopping experience
Brands that fix search intent see fewer zero result searches and a measurable lift in search to purchase conversion, alongside far less manual work for the merchandising team keeping listings current.
For more details, click the relevant case study link below.
View Eva AI Sales Assistant case studyThis runs on the identical vector index and retrieval layer already in production behind Eva AI's product recommendations, extended to power a search bar instead of a chat conversation — which is largely a difference in interface, not underlying infrastructure.
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Want this one built for your business?
We will walk you through the architecture, what it takes to integrate with your systems, and a realistic timeline — before anyone signs anything.