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Assessment systems continue to face persistent challenges related to grading bias, inter-rater inconsistency, and limited auditability despite the widespread adoption of rubric-based evaluation frameworks. RubiQuant is an Excel VBA-based quantitative rubric intelligence system developed to transform subjective grading into structured algorithmic computation. The system requires evaluators to quantify measurable indicators such as percentage completeness, number of errors, weighted error cost, and rubric weight allocation before marks are generated. These quantified inputs are processed through an embedded computational engine that standardizes scoring and enhances transparency. By converting qualitative rubric descriptors into measurable parameters, RubiQuant strengthens governance, fairness, and accountability in evaluation processes. The innovation aligns with ESG principles, particularly governance, by improving traceability and defensibility of grading decisions. Pilot implementation demonstrates improved inter-rater consistency and reduced subjective variability. RubiQuant offers a scalable, cost-effective solution for educational institutions and professional certification bodies seeking bias-reduced and transparent assessment systems.