Authors - Max Angelo D. Perin, Lenie B. Maligmat, Darrel A. Cardana, Renante S. Digamon, Joan Mae G. Lagumbay, Cecilia T. Gumanoy Abstract - The Quality Assurance Office of a Philippine state university campus conducts 7S evaluations across all offices each semester, producing numeric scores and written evaluator comments. Consolidating the narrative comments has depended on manual review, which is time-consuming across more than a hundred offices per cycle. This paper describes a two-phase AI-assisted analytics pipeline. Phase 1 retrieves audit records from a MySQL database via a stored procedure, formats them with a Python ETL script, and submits them to Grok (xAI) to draft scorecards and action items; evaluators then review the drafts be-fore consolidation into the official PDF report. Phase 2 parses the validated PDF with Python to extract structured fields and compute descriptive statistics, office rankings, a priority index, and TF-IDF text clustering. Applied to the November 2025–January 2026 cycle (112 offices; 107 scored, 5 with no submission), most units cluster in the moderate-to-great compliance range while a meaningful minority fall below threshold. Among the top 25 priority offices, Standardize (20/25) and Safety (19/25) are the most frequently flagged dimensions. The pipe-line shows that AI assistance structured around human review can accelerate QA consolidation while preserving evaluator accountability.
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