Implementing AI on business data, especially streaming data (aka data in motion), and getting actionable predictions from it is difficult, time-consuming, and expensive. That is why we at airt* have a simple, quick, and affordable solution that can be implemented and deployed even without internal AI expertise. Our low-code AI PaaS handles all the heavy lifting of data (pre)processing and uses our patented neural network architectures to predict future events to be streamed, which can be used for both standard business predictive analytics and automatic regression and stress testing of the system. Our generative AI is incorporated into a framework that simplifies the development, testing, and management of AI-powered microservices for streaming data, and which is designed to be scalable and user-friendly, utilizing modern programming paradigms to accelerate development and testing by producing highly efficient code. Furthermore, given the complexity and scale of the data, models, and compute resources involved in AI deployment, we are making sure such resources are used at maximum efficiency. Our GPT-based models allow for a wide range of business problems by enabling predictions of any combination of events and/or non-events (e.g. can be used by developers for advanced testing scenarios, or by the business to predict end-customers' behavior). They also surpass in accuracy competing AI models from both academic and industry giants IBM and Google and are dramatically reducing computing resources by using 100-300 times fewer parameters than current industry standards. This way, we both reduce the carbon footprint of deep learning and make our solution affordable to everyone, from the smallest webshop to the largest financial institutions. *airt [Scot. eyrt] = to direct, guide