# Atlas AI ## Real-time 1:1 personalized experiences Atlas AI delivers personalized experiences in real time. By leveraging your vectorized product data, it extracts key features and attributes using embeddings. This builds a network of neural models that serves as an in-depth representation of your entire product assortment. ## How Atlas AI works ### Data training * The model is trained on your vectorized product data. * It uses embeddings to capture every product feature and attribute. * A network of neural models is created, representing your product range in detail. ### Contextual product mapping * Products are grouped by similar contexts to form a visual "map" of your assortment. * Distances on the map indicate differences between products along specific dimensions. * Initial layers might group products by category (e.g., smartphones); deeper layers differentiate features like screen size and price. [![Deep learning illustration](https://www.fact-finder.com/blog/wp-content/uploads/2023/07/Deep_Learning-01-2.png)](https://www.fact-finder.com/blog/wp-content/uploads/2023/07/Deep_Learning-01-2.png) ### Granular understanding * Multiple neural map layers allow the model to grasp product relationships at a granular level. * The depth of mapping depends on the complexity of your catalog, resulting in a human-like understanding of your assortment. ### Interactive learning * When shoppers browse your store, they interact with the Atlas AI model rather than static product listings. * The model learns from every interaction — even a single click — to improve relevance instantly. ## Atlas AI advantage Atlas AI goes beyond traditional segmentation with a three-step approach designed to deliver precise, personalized shopping experiences: ### 1. General relevance based on shoppers' behavior * What it does: Atlas AI optimizes search results and recommendations based on shoppers' behavior, including search queries and interaction patterns. * Benefit for you: Reduces the need for manual search optimization. ### 2. Relevance based on the current shopping intent * What it does: Adapts results based on the shopper's immediate session behavior and signals of buying intent. * Benefit for you: Provides a natural shopping experience by displaying only contextually relevant products. ### 3. Relevance based on personal preferences * What it does: Analyzes individual preferences through past purchases, product views, and specific interaction patterns. * Benefit for you: Enhances precision, resulting in a personalized shopping experience that boosts conversion rates. ## Short summary Atlas AI transforms customer engagement by using advanced neural networks to understand your product assortment in a human-like way. Its layered approach to mapping and relevance creates a tailored, real-time shopping experience that drives higher engagement and revenue. Want to learn more? Watch our short video below: --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.fact-finder.com/docs/readme/atlas-ai.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.