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Modern AI grapples with critical challenges, from transparency and traceability to resource inefficiencies and development expenses. A significant portion, about 70%, of AI workflow is consumed by data manipulation. This entails refining data for more effective AI training. Our innovative model minimizes the need for data cleaning and handling while enhancing inference quality. Using a proprietary equation-oriented and physics-based approach, our model remains data-constrained, preserving data integrity and minimizing the risk of bias. Importantly, it fosters traceability and transparency. This discussion introduces a novel data approach, addresses AI concerns, and offers alternative data processing and analytics perspectives.