Top 10 AI Store Builders for Eastern Europe

2026. Google intends to implement common UCP standard for AI shopping



Google has officially unveiled the Universal Commerce Protocol (UCP) - an open standard that will make it easier for AI agents to search and shop online. The idea is that in the near future, you'll be able to ask Gemini or another chat app, for example, to "buy groceries as usual" and wait for delivery. The new protocol was developed in collaboration with industry leaders, including Shopify, Walmart, Target and eBay, and is compatible with existing industry protocols such as the Agent Payments Protocol (AP2) and the Model Context Protocol (MCP). One of the first practical applications of UCP will be direct checkout within Google's AI search and the Gemini app. US users will be able to purchase products from select retailers while researching products, using payment and shipping information from Google Wallet or PayPal.


2025. ChatGPT helps with online shopping



OpenAI has updated the web search feature in ChatGPT to improve the online shopping experience for users. Chat will now provide personalized product recommendations, complete with images, reviews, and direct shopping links in categories such as fashion, beauty, home goods, and electronics, in response to specific questions. The updated search feature will be ad-free, and the company will not receive commissions on purchases made through the ChatGPT platform. Results will be generated independently, based on structured metadata from external sources, such as prices, product descriptions, and reviews.


2020. Facebook unveiled AI tool for recognizing products in photos



Facebook has launched a tool called GrokNet, which can identify different product categories in photos using artificial intelligence technology. It recognizes tens of thousands of different attributes, such as brands, colors, and sizes. This tool is already integrated into Facebook Marketplace, allowing users to quickly create product descriptions. For example, when a seller uploads a photo of their sofa, the site suggests characteristics such as "black," "leather," and "sectional sofa." The AI ​​system "learned" image recognition using a database of approximately 100 million user-submitted photos in Marketplace.


2016. Sift Science - AI-based anti-fraud service


Online merchants lose millions due to fraud (payments made with stolen or counterfeit bank cards that have to be refunded after the goods have already been shipped). Therefore, anti-fraud systems have long been widespread. They determine the likelihood of an unauthorized purchase based on various parameters (country, issuer, buyer details, their website activity history, etc.). Having a large database of good and bad buyers' histories allows one to determine the likelihood of fraud. And this is a task that machine learning is best suited for. This is precisely the system implemented by the American startup Sift Science. The startup was founded in 2011 at the Y Combinator incubator. Today, it is a profitable business with thousands of clients.


2015. Deepomatic raises $1.4M to recognize clothing in images



French startup Deepomatic has received $1.4 million in funding from Alven Capital and several angel investors to develop its computer vision technology, which specializes in clothing recognition. The idea is to complement images of fashionable clothing with e-commerce links. For example, if you like the shoes on the girl in the picture, you click and are taken to the online store where those shoes are sold.