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AI in Practice: API or Your Own Model?

AI isn’t magic, but when used in the right way, it can create a real competitive advantage. This week’s guest is Vlad-Adrian Ilie , Senior AI Lead at CUBE , joining for a practical conversation about one of the most important decisions companies face when building with AI: when should you use an existing AI API, and when does it make sense to build your own model? Vlad shares lessons from real-world AI projects across healthcare, manufacturing, drone technology, computer vision, and enterprise solutions. He explains why starting with an API-based prototype is often the fastest and smartest way to validate an idea — but also why APIs aren’t always enough when the problem requires highly specialized data, greater precision, control, or strict data privacy. In this episode, we discuss: •⁠ ⁠How to decide between an AI API and building your own machine learning model •⁠ ⁠Why data quality is often more important than the model itself •⁠ ⁠How synthetic data can help when real-world datasets are limited •⁠ ⁠Data governance, sensitive information, and European regulation •⁠ ⁠How combining multiple AI models can improve accuracy •⁠ ⁠Practical AI applications in coding, sales, computer vision, and automation •⁠ ⁠Where AI still struggles to replace human judgement •⁠ ⁠Why context becomes critical when building and deploying AI agents This is a practical episode for founders, developers, product leaders, and business decision-makers who want to move beyond AI hype and understand how the technology can create real value in production.