Search in Voyado Elevate combines several intelligent systems that work together to understand both your products and your customers’ intent. Instead of relying only on keyword matching, Elevate uses structured product knowledge, query analysis, and ranking logic to deliver relevant results. Understanding how they work together helps you optimize search performance and troubleshoot unexpected behavior. This article introduces the core components that power search in Elevate.
How Elevate understands and categorises your product data
It all starts with your product data.
To deliver accurate search results and relevant recommendations, Elevate needs to understand what your products actually are. This understanding is powered by ontology. Instead of relying only on the words in your product feed, ontology creates a structured model of product types, attributes, and relationships that reflect how products are understood in retail.
This structure allows Elevate to recognize concepts such as product types, materials, colors, and occasions. Because products are placed into a network of connected concepts, the system can match customer intent even when the wording in the search query differs from the wording in the product feed.
The Elevate Classifier
The Classifier automatically analyses and enriches your product data when it is sent to Elevate. It interprets the information in your product feed and maps each product to a structured product model within the Knowledge Graph.
By extracting attributes from structured fields, product descriptions, and sometimes images, the Classifier ensures that Elevate understands what each product is and how it should behave in search. The quality of the classification depends heavily on the quality and consistency of your product data.
The Elevate Knowledge Graph
The Knowledge Graph structures products, attributes, and concepts into a connected model that allows Elevate to understand meaning rather than only text. It organizes product types into hierarchies and connects related concepts so that search queries can be interpreted more accurately.
Because these concepts are linked, customers can search at different levels of specificity and still receive relevant results. The Knowledge Graph also supports multilingual search by connecting equivalent concepts across languages.
The Query analyzer
The Query analyzer interprets your customers’ search queries in real time. Instead of matching text directly, it breaks each query into meaningful concepts such as product type, brand, color, and contextual attributes.
These concepts are then matched against the classified product catalogue. If no exact match exists, Elevate can automatically relax less important parts of the query to return the most relevant results instead of showing an empty search page.
Troubleshooting search in Elevate
Search behavior in Elevate is influenced by several layers working together, including product classification, query interpretation, and ranking logic. When investigating unexpected results, it is important to review each of these layers systematically.
Most search issues can be traced back to product data quality, query interpretation, or ranking behavior. By checking the search phrase, validating product data, and reviewing configuration settings, you can usually identify the root cause of the issue.