Malachyte developed an AI-powered platform to solve the challenge of recommending products to new users. This personalization, crucial for online services, was inspired by advances in large language models. The company utilized Google Cloud tools like Bigtable and Managed Service for Apache Kafka to build its advanced recommendation algorithm. Malachyte's breakthrough involved applying neural networks with attention mechanisms, similar to those in LLMs, to retail interactions. They sought to predict a user's next desired item by analyzing their sequence of on-site activity. This approach addresses the "cold start" problem by using every interaction to refine a user's profile vector. As new data is added, predictions improve, and the overall model benefits from anonymized user information. Malachyte's system updates user profiles in milliseconds, enabling real-time recommendations. Unlike traditional methods requiring extensive historical data, Malachyte focuses on session interactions for greater personalization and privacy. This multimodal system encodes user data, embedding products into the same vector space for accurate recommendations. Google Cloud's real-time AI infrastructure, including Kafka for streaming events and Bigtable for quick lookups, allows for rapid updates and recommendations. This scalable architecture is vital for Malachyte's ability to deliver personalized experiences efficiently.