Site search is where high-intent shoppers go — and where traditional keyword search quietly loses sales. If someone types "warm jacket for hiking" and your engine only matches exact words, it misses the perfect product tagged "insulated outdoor coat." AI-powered semantic search fixes this by understanding meaning, not just keywords.
Keyword search vs semantic search
Keyword search matches strings. Semantic search matches meaning by converting products and queries into vectors — numerical representations of meaning — and finding the closest matches. So "laptop for video editing" can surface high-performance machines even if those exact words never appear in the product copy.
How vector search works
- 1Embed your catalog — each product is converted into a vector capturing its meaning.
- 2Store the vectors — they're indexed in a vector database for fast similarity search.
- 3Embed the query — the shopper's search is converted into a vector the same way.
- 4Find nearest matches — the engine returns the products closest in meaning.
- 5Rank and blend — combine semantic relevance with business rules like stock and margin.
Why it lifts conversion
- Handles natural language — shoppers can describe what they want in their own words.
- Tolerates synonyms and typos — fewer dead-end, zero-result searches.
- Understands intent — surfaces the right products even with imperfect queries.
- Powers discovery — "more like this" and richer recommendations come for free.
Implementation notes
- Combine semantic search with traditional filters (price, brand, availability) — shoppers still want both.
- Keep embeddings fresh as your catalog changes.
- Monitor zero-result and low-engagement queries to find gaps.
- Integrate cleanly with your platform — on Shopware, the Store API makes this straightforward.
We build AI-powered search and discovery into Shopware stores using modern embedding and vector-search techniques. Explore our custom plugins & AI work or get in touch.