Abstract: Systems and methods of performing airline seat brand identification and fare grouping are presented. In one exemplary embodiment, a first network node device receives ticketing data and brand reference data, determines whether an airline is a contributing or non-contributing source of brand reference data, and executes corresponding processing sequences. For contributing airlines, mapping logic assigns an airline brand and a first AI model identifies associated brand amenity attributes. For non-contributing airlines, a second AI model infers an airline brand from ticketing-related variables, and the first AI model derives corresponding brand amenity attributes. The assigned and inferred data are processed through a multidimensional clustering algorithm based on mixed numerical and categorical similarity metrics to generate benchmark groups of airline brands and a brand-based benchmarking dataset.