Developing and validating new AI concepts can be prohibitively expensive when relying solely on real-world data collection. This paper proposes a flexible simulation-to-real framework that leverages cost-effective game-based environments as an initial testbed for rapid AI hypothesis testing. Our approach employs a modular ensemble of specialized Convolutional Neural Networks for robust feature extraction, while another simple yet efficient classifier (e.g. Random Forest classifier) provides high-level recommendations. Through iterative evaluation and refinement in simulation, only the most promising methods advance to resource-intensive real-world trials. By reducing costs and accelerating development, this framework is particularly well suited for diverse applications ranging from assistive technology to robotics. Empirical results demonstrate strong accuracy across multiple tasks and underscore the effectiveness of a modular design that can be easily adapted to new domains. The proposed pipeline ultimately aims to streamline AI research by enabling faster decisions about which innovations merit real-world deployment. The code is available at the following repository: https://github.com/sqdArtemy/mspv