Vietnam is currently one of the fastest-growing economies in the world, with companies in a rapid race to become one of the best. However, the lack of a comprehensive ranking in terms of corporate performance in Vietnam makes it a challenge to accurately measure the success of businesses as a whole picture; relying on individual ranking systems might not be sufficient in measuring all aspects. This study addresses the limitations of individual ranking systems by creating a proof-of-concept comprehensive framework ranking system for 243 Vietnamese companies using an adjusted Reciprocal Rank Fusion (RRF) algorithm that integrates twelve domestic and international business rankings. The Adjusted RRF incorporates AI-generated reputation scores from the Large Language Model GPT-o4 to determine source weights with human revision and apply time-decay factors to account for the temporal relevance of ranking data. The application of Adjusted RRF enables significant ranking shifts, with the adjusted model enhancing the alignment between the ranking score and real-life firm-level attributes compared to the original RRF. Statistical analysis using multiple linear regression and predictive power score testing identified total assets, net revenue, and number of employees as significant predictors of ranking performance, with predictive power scores exceeding 0.25 for all variables tested. The potential of the adjusted ranking model provides a more accurate representation of current business performance in the Vietnamese market, offering a valuable reference for job seekers, policymakers, and recruiters in identifying reliable companies in Vietnam.