What is semantic search in fashion?
Semantic search finds information based on meaning and intent rather than exact keyword matches, using AI representations called embeddings.
In short
Semantic search is a search method that understands the meaning of a query and the content being searched, rather than relying only on matching words. In fashion it lets shoppers, buyers and staff find relevant products or documents using natural descriptions, synonyms or occasions instead of exact product terms.
How does it work in practice?
Product texts, attributes and documents are converted into embeddings, numerical vectors that capture meaning. These are stored in a vector database. When someone searches, the query is converted in the same way, and the system returns the items whose vectors are closest. Many systems combine this with keyword search and filters such as size, price and stock, an approach known as hybrid search.
Fashion use cases include:
- Webshop search for occasion or style queries like relaxed linen outfit for holidays.
- B2B ordering, where buyers search a collection by theme or category concept.
- Multilingual search, matching queries and products across languages.
- Internal search across tech packs, policies and supplier documents.
Why does it matter for fashion businesses?
Customers rarely use the same terms as product teams. One shopper types pants, another trousers, a third describes a look. Keyword search often returns poor or empty results in such cases, which leads to lost sales. Semantic search narrows this gap and makes large assortments easier to navigate.
How does AI use it?
Semantic search is the retrieval engine behind many AI applications, including retrieval-augmented generation, AI search and recommendation. Multimodal embeddings extend it to images, allowing combined text and visual queries.
Common pitfalls
- Dropping keyword search entirely, which hurts exact queries like style numbers.
- Weak product content, since meaning cannot be found if descriptions are thin.
- Ignoring availability, returning relevant but out-of-stock items.
- No evaluation with real queries from customers or buyers.
Frequently asked questions
What is the difference between semantic search and keyword search?
Keyword search matches the exact words in a query. Semantic search matches meaning, so it can find relevant results even when different words or descriptions are used.
Does semantic search improve conversion in fashion ecommerce?
It can help by reducing failed searches and surfacing more relevant products. The effect depends on product data quality, how results are ranked and whether search is combined with filters and stock data.