MVP Eval
Before trying the MVP, please start with the Eval Guide above and then use the email login option, it is passwordless (code will be sent to your email address). Social logins are not supported on this page.
Product is at MVP stage (Minimum Viable Product) and ready for client review and trials.
Our objective is personalized conversational shopping. shopper GPT allows consumers to shop using natural language queries (a shopping LLM, like chatGPT is for general internet queries), search for products matching any color in the color spectrum and search for similar items by submitting images with a fashion target included.
The MVP includes a catalog of women’s fashion items. There might be a few men’s items mixed in, but not many. Also, we did not add accessories (hats, handbags, etc.). We can ultimately support any vertical market, but some will be a better fit than others. Conversational e-commerce search will apply ubiquitously to all online shopping sites (in our humble opinion 😊 and there are 24M of them 😲).
Once the initial personalization is complete, it allows shopper GPT to tailor responses to each individual user (this is also known as Hyper-Personalization). Objective is to respond to each shopper in their preferred manner, with their preferred styles, colors, materials, and even conversational styles. And shopperGPT continues to learn and personalized based each incremental user’s action.
shopper GPT is not going to deliver a perfect match, at least hardly ever. The goal is to deliver the nearest product matches that exist in the current product catalog. The larger the catalog, the closer the matches. But even without a huge product catalog, if the query fairly represents the targeted client product catalog, it will deliver good results.
shopper GPT will be offered on a secure API basis to B2B e-commerce clients and as an plug-in application for platforms like Magento, Bigcommerce, Salesforce and Shopify.
Our objective is personalized conversational shopping. shopper GPT allows consumers to shop using natural language queries (a shopping LLM, like chatGPT is for general internet queries), search for products matching any color in the color spectrum and search for similar items by submitting images with a fashion target included.
The MVP includes a catalog of women’s fashion items. There might be a few men’s items mixed in, but not many. Also, we did not add accessories (hats, handbags, etc.). We can ultimately support any vertical market, but some will be a better fit than others. Conversational e-commerce search will apply ubiquitously to all online shopping sites (in our humble opinion 😊 and there are 24M of them 😲).
Once the initial personalization is complete, it allows shopper GPT to tailor responses to each individual user (this is also known as Hyper-Personalization). Objective is to respond to each shopper in their preferred manner, with their preferred styles, colors, materials, and even conversational styles. And shopperGPT continues to learn and personalized based each incremental user’s action.
shopper GPT is not going to deliver a perfect match, at least hardly ever. The goal is to deliver the nearest product matches that exist in the current product catalog. The larger the catalog, the closer the matches. But even without a huge product catalog, if the query fairly represents the targeted client product catalog, it will deliver good results.
shopper GPT will be offered on a secure API basis to B2B e-commerce clients and as an plug-in application for platforms like Magento, Bigcommerce, Salesforce and Shopify.