Our advanced AI search feature utilizes the power of Natural Language Processing (NLP), a branch of artificial intelligence that focuses on understanding the complexity of human language by adding structure and meaning to unstructured data. In the case of our app, this unstructured data refers to shoppers' search queries, which is critical for understanding the intent behind the search queries and extracting the necessary information to provide the most suitable products for them.

The advanced AI-powered search feature includes several key features that help improve the accuracy and relevance of search results for e-commerce stores. Among these features are

Name Entity Recognition involves identifying different attributes of a product, such as color, size, and price, and extracting them from the shopper's search query. By analyzing these attributes, we reap the benefits of:

Here is an example demonstrating how our AI model can detect and extract the different attributes of a product when a shopper enters a search term.

Semantic Search revolutionizes the way data is retrieved by tapping into the power of intent and context embedded within search queries. By deciphering the shopper's needs, Semantic Search:

For instance, if a shopper is looking for "women's white sneakers for climbing $30-$50" they have specific requirements in mind. Our engine analyzes the search query and filters the results based on the shopper's specified criteria:

By doing so, the engine can present a variety of women's sneakers that match the shopper's specific needs and preferences, including high-top sneakers, trail running shoes, cross-training shoes, and more, designed for climbing activities or outdoor activities, available in white, and priced between $30 and $50. The AI's ability to understand the shopper's intent and locate suitable products enhances their search experience by making it more efficient and effective.

We have integrated an intelligent system that recognizes and processes synonyms and variations within user search queries, enhancing search result accuracy and relevance. Our model analyzes these data sources to identify patterns and relationships between different search terms and processes various possible representations of a word to generate relevant synonyms. By incorporating this feature into our search system, we can:

A feature that helps enhance the search experience is continuously suggesting keywords based on shoppers' behaviors, preferences, and past interactions.

Benefit for merchants:

Benefit for shoppers:

Typo tolerance is a feature that allows a system to recognize and correct typing errors made by shoppers. Typo tolerance is fundamental to modern search experiences, and it reduces frustration caused by small typing mistakes. It can save time and effort for shoppers who may not have realized they made an error and can improve the accuracy of search results. For example, if a shopper misspells a word in a search engine, the search engine will suggest the correct spelling or provide results that match the intended word.

A dynamic ranking system that adjusts product display order based on key performance metrics like conversion rate, add-to-cart rate, and click-through rate. Some benefits provided by this ranking system are:

Search Personalization encompasses customizing the shopping experience for individual shoppers by providing personalized product recommendations and tailored search results. This approach personalizes the browsing journey for each shopper according to their behaviors, preferences, and past interactions. Benefits of a personalized search:

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If you have any questions or need further assistance, please do not hesitate to contact our dedicated support team at [email protected].