Detecting compatibility mismatch by generative artificial intelligence is described. Compatibility data is obtained (e.g., by accessing a database). The compatibility data is associated with a compatibility between items (e.g., items and categories of vehicles or an item and another item) and includes a list of recommended compatibilities between the items and a user reported compatibility for at least one item. A machine learning model is generated for detecting a compatibility mismatch between a first item and a second item and/or between an item and a category of vehicle. At least a portion of the compatibility data is provided as input to generative artificial intelligence to generate the machine learning model. An update to the list of recommended compatibilities is determined based on the detected compatibility mismatch.

An e-commerce platform server includes a communication device, a memory storing instructions, and a processor executing the instructions stored in the memory, wherein the processor, by executing the one or more instructions, registers supply products of a supplier to be sold in a platform service in a supply product DB, registers sellers in a seller DB in a layer structure including an upper seller and at least one sub-seller subordinate to the upper seller, registers sale products of a top seller of the layer structure in a sales product DB, wherein the sale products are at least one of the supply products registered in the supply product DB, and generates an online shop of a sub-seller including at least one of the registered sale products of the top seller, wherein the sub-seller is subordinate to the top seller, is disclosed.

A system includes a processor that is configured to receive user information, obtain store information, generate a recipe based on the user information and the store information, present the generated recipe to a user, and guide the user regarding the required ingredients for the recipe.

A method for providing a personalized shopping and styling experience using an AI-Interface application is provided. The method includes presenting a user app interface on a user device associated with a user to receive user data comprising user shopping and styling preferences, shopping habits, and images of the user's personal wardrobe inventory. The method also includes providing a virtual closet for the user, wherein the virtual closet comprises processed images of the personal wardrobe inventory of the user. The method further includes providing a personalized product feed source by filtering product data. The method additionally includes providing a personal stylist interface that allows stylists to access the virtual closet of the user. The method includes analyzing using an AI model, the user preferences and shopping habits, sales history, browsing history, and the selected items, wherein the AI model continuously learns and generating personalized product recommendations to the user.

The system may create marketing campaigns based on inventory data and/or sales data for a product. The system may compare the inventory data for the product or the sales data for the product to inventory threshold data or sales threshold data, respectively. The system may adjust a marketing campaign for the product based on a relationship between at least one of the inventory data and the inventory threshold data, or the sales data and the sales threshold data. The marketing campaigns may be further based on weather data, traffic data, event data, seasonal data, holiday data, calendar data, trend data and/or any other data.

A method for electrical component matching. The method includes receiving an input indicative of a given electrical component for which at least one matching electrical component is required to be identified; inferring specifications of the given electrical component using the input; determining a first set of electrical components from amongst the plurality of electrical components using rule-based cases applied corresponding to the specifications; determining a second set of electrical components from amongst the plurality of electrical components using machine learning; generating raw output data comprising product data of the electrical components of the first set and the second set; and processing the raw output data for generating processed output data.