In B2B commerce, search decides revenue: anyone selling tens of thousands of items has no navigation structure that covers every need. According to SQLI, 76 percent of B2B buyers navigate primarily via search – and that is exactly where many shops fail at one pattern: the buyer describes a problem, the search expects a product name.
The application-search problem
"Heavy-duty water transport" should find industrial pumps – even if the search term never contains the word pump. "Seal resistant to hydraulic oil, -30 degrees" should return the right material classes, not zero results. Keyword matching cannot do this by design; it finds character strings, not intent. The semantic layer – embeddings that map closeness in meaning – solves exactly that. On its own, however, it would be negligent in B2B.
Why hybrid is the only serious approach
| Layer | Job | Example |
|---|---|---|
| Exact matches | SKUs, DIN/ISO standards, manufacturer designations without detours | "DIN 933 M8x40" → exactly this item, instantly |
| Semantics (vector) | Understand application and problem descriptions | "heavy-duty water transport" → industrial pumps |
| ERP context | Framework agreements, customer-specific prices and availability factor into ranking | in-stock items from the framework agreement ranked first |
A pure vector approach that answers an exact SKU "approximately" destroys trust faster than semantics can build it. Hence the rule: exact matches always win, semantics complements, context sorts. Service providers report 25 to 45 percent higher conversion rates for such hybrid implementations – the figure comes from vendor material (SQLI) and should be read accordingly, but the direction matches our project experience: abandoned searches are B2B's most expensive silent revenue loss.
The real project is in the data
The uncomfortable truth: semantic search can only understand what is in the data. If application areas, compatibilities and problem solutions do not exist anywhere as attributes, the best vector search has nothing to rank. That is why enrichment and search are one package: first, application-related attributes are extracted from spec sheets and descriptions using AI, then the search is built on top of them. Buying only "semantic search" gets you a demo – building both halves gets you a sales channel.
The stack: standard technology instead of a search product
Technically, hybrid search in 2026 is no longer rocket science: OpenSearch or PostgreSQL with pgvector for the vector layer, embeddings from interchangeable models, classic inverted-index search for exact matches, a re-ranking step for context. It runs on your own infrastructure, GDPR-compliant and without SKU-based search product licenses – the same stack we use to build web apps and RAG systems.
Whether your product data already meets that prerequisite is what our data readiness audit clarifies – details on the PIM services page.
