Data management and storage or data access and data retrieval are two crucial tasks in the AI system among various challenges when the amount of data, datasets, inputs, and features increase. Vector-space models constitute the cornerstone for handling high-dimensional data, allowing people to conduct fast offers of likeness and data-driven analyses for various areas, including NLP and computer vision. In this paper, a theoretical approach toward the effective management of data is presented based on vector bases along with efficient mathematical algorithms. The application of this framework in an e-commerce recommendation system is supported by a case study showing that there is better efficiency in the system’s retrieval rate and accuracy. Comparison with conventional approaches for data optimization reaffirms the possibility of this model to revolutionize new AI efficacy benchmarks.