AI-driven search – Traditional e‑commerce search bars are built around keywords. They return results based on exact text matches and simplistic rules. That approach fails when users describe their intentions in natural language (“show me outfits for a summer wedding”) or search by vibe (“retro aesthetic” or “cozy hygge décor”). To deliver the intuitive experience shoppers expect in 2025, retailers need AI‑driven search—a new generation of recommendation engines that understand what customers mean and surface relevant products, not just literal matches. This article explores how to build such systems using vector search and embeddings, and why doing so can dramatically increase average order value (AOV) and customer satisfaction.
Why keyword search falls short
Keyword‑based search engines rely on matching exact words or predefined synonyms. They can’t grasp the difference between Apple the fruit and Apple the technology company, nor can they infer that a query for “cute sundress” might also be satisfied by a “floral midi dress.” Vector search solves this problem by converting both products and queries into numerical vectors that capture the semantic meaning of the text. The system then measures similarity between vectors instead of looking for matching substrings. As a result, vector search can identify related objects without requiring exact text matches. It’s widely used in applications like recommendation systems, image retrieval and modern search engines.
Limitations of simple search
Poor context understanding: Keyword search does not interpret the meaning behind queries. A query for “casual summer outfit” may return only products with those words in the title, missing relevant items like lightweight jeans or espadrilles.
Synonym blindness: Different words with similar meanings (“purchase” vs. “buy”) require manual synonym dictionaries. Vector search understands that synonyms share similar vector positions.
Lack of personalization: Keyword search treats all users the same. It can’t adapt to individual preferences or clickstream behavior, resulting in generic results and lower conversion.
By switching to AI‑driven search, retailers can interpret user intent, deliver relevant results and guide customers toward complementary products that increase basket size.
What are vector embeddings and vector search?
At the heart of AI‑driven search are vector embeddings. Embeddings are numerical representations of text, images or other data that capture their meaning and context. Language models like BERT, GPT or Sentence Transformers translate complex data—such as product descriptions, user queries and browsing behavior—into high‑dimensional vectors. Semantically similar items cluster together in this vector space, allowing systems to recognise relationships that go beyond rigid keyword matching.
OpenAI’s documentation notes that embeddings measure the relatedness of text strings and are commonly used for search, clustering and recommendation tasks. When a query like “lightweight summer footwear” is converted into a vector, the system recognises that cork‑sole sandals and mesh flats are relevant even if those terms don’t appear in the query. Vector search retrieves products based on their position in this semantic space, finding the nearest neighbors to the query vector. These candidates are then ranked using additional signals like clickstream behavior, past purchases and real‑time inventory.
How vector search works
Vector search involves four main steps:
Data representation: Convert all products and queries into vectors using an embedding model. Models such as OpenAI’s
text‑embedding‑3or open‑source alternatives (e.g., Sentence Transformers) turn text into arrays of floating‑point numbers. For multi‑modal data, you can use vision models like CLIP to embed images.Similarity measurement: When a user submits a query, convert it into a vector and compute similarity between this query vector and every product vector using metrics such as cosine similarity. The closer two vectors are in angle, the more related they are.
Indexing: Storing all embeddings in a vector database allows you to perform approximate nearest neighbor searches efficiently. Options include FAISS, Milvus, Pinecone and Weaviate. These systems use algorithms like k‑nearest neighbors to quickly retrieve the most similar vectors.
Result ranking and blending: Once the top candidates are retrieved, apply additional ranking factors (stock availability, price, user preferences) and blend semantic results with traditional filters (brand, price range) to produce final results. This hybrid approach ensures relevance and business viability.
AI‑driven search in e‑commerce
AI‑driven discovery transforms every step of the online shopping journey. A report on product discovery notes that modern shoppers often start with goals or needs rather than specific products. Effective discovery engines must interpret natural language, identify relevant attributes and surface products that align with shopper intent. Vector embeddings make this possible by powering semantic search and recommendation systems.
Key benefits of vector search and AI‑driven discovery include:
Higher conversion and AOV: Smarter search experiences drive higher conversion rates and increase revenue per visitor. By matching users with relevant products—even when queries are vague or off‑beat—vector search encourages larger baskets.
Personalized recommendations: Recommendation engines use vector embeddings to encode both products and user preferences; as customers interact with the store, their preference vectors evolve. Comparing this vector to product embeddings surfaces the most relevant items in real time.
Better handling of long‑tail queries: Vector search understands rare or descriptive phrases, returning high‑quality results for natural‑language inputs.
Fallback recommendations: When there’s no exact match, the system can suggest semantically related items.
These capabilities transform search from a basic utility into a powerful engine for discovery, engagement and conversion.
Building your own recommendation engine
Creating a bespoke recommendation engine involves more than turning on a plug‑in. You need to combine embeddings, vector databases and business logic to create a cohesive experience.
1. Prepare and embed your data
Start by consolidating product information (titles, descriptions, attributes), user data (past orders, clicks, wishlists) and any relevant metadata (brand, price, availability). Preprocess text by cleaning and normalising it (lowercasing, removing special characters) and enrich product data with category and brand names. Then generate embeddings for all products using an appropriate model and store them in a vector database.
2. Store vectors in a database
Vector databases like FAISS, Milvus, Pinecone and Weaviate are designed for approximate nearest neighbor search. They index vectors and enable fast similarity searches even on millions of products. Choose a database based on your scaling needs, latency requirements and infrastructure.
3. Handle search queries
When a user enters a query, convert it into an embedding using the same model. Use your vector database to retrieve the closest product vectors. Combine semantic similarity with traditional filters such as price range, size and colour to respect business rules. For example, a query like “outfits for a summer wedding” can retrieve dresses, suits and accessories that match the season and style even if they don’t share keywords with the query.
4. Personalise with behavioural data
Embeddings are not limited to products. You can embed user sessions, clickstream data and purchase histories to build a preference vector for each shopper. Compare these vectors with product vectors to recommend items aligned with individual tastes. Over time, as users interact with the site, update their preference vectors to reflect evolving preferences. This continuous learning is the key to delivering “for you” recommendations that boost AOV and loyalty.
5. Iterate and monitor
AI systems need regular tuning. Monitor search and recommendation performance using metrics such as click‑through rate, conversion rate and average order value. Refresh embeddings periodically as new products are added or descriptions change. Experiment with hybrid approaches that blend semantic search with keyword filters and business rules. Collect qualitative feedback from shoppers and adjust ranking algorithms accordingly.
Best practices
The Datos report offers several recommendations for harnessing vector embeddings effectively:
Query understanding and expansion: Interpret and enrich user input to improve accuracy and recall. For example, expand “vintage beach look” to include related terms like “retro swimwear” or “boho maxi dress.”
Semantic matching: Match queries to products even when keywords don’t overlap. This is essential for intent‑based searches.
Long‑tail handling: Return relevant results for uncommon or natural language queries. Vector search thrives on long‑tail requests that previously returned empty results.
Fallback suggestions: Surface semantically related items when no exact match exists.
Auto‑suggestions: Recommend queries or refinements based on similarities to past successful searches.
Reranking with behavioural patterns: Use deep contextual similarity and user behaviour to order results for maximal relevance.
Conclusion
Implementing AI‑driven search isn’t just about adopting new technology—it’s about rethinking the entire product discovery experience. By embedding your catalog and user data into a semantic vector space, you empower shoppers to search by intent rather than exact keywords. Vector search retrieves the most semantically relevant items, while personalised recommendation engines use behavioural embeddings to tailor suggestions and cross‑sells. The result is a shopping experience that feels conversational and intuitive, leading to higher conversion rates, increased AOV and deeper customer loyalty.
For retailers already experimenting with AI, see our guides on AI‑powered WordPress websites and social commerce AI for Shopify for more ideas on integrating intelligent search and recommendation tools into your platforms. Combining AI‑driven search with personalised recommendations is the next step toward building truly customer‑centric e‑commerce experiences.
