Copulas hold significant importance in the context of multivariate statistics and risk management, as they model the relationship between variables irrespective of their marginal distributions. In this talk, we aim to introduce a new class of multivariate copulas named index-mixed copulas, that are in a sense a generalization of copula mixtures, and show a remarkable degree of analytical tractability. Properties investigated include measures of concordance such as Spearman's rho, Kendall's tau, and concordance orderings. As the construction is based on a stochastic representation, sampling algorithms can be given as well. An interesting feature of index-mixed copulas is that they allow one to provide an interpretation of the family of Eyraud-Farlie-Gumbel-Morgenstern (EFGM) copulas, which are popular due to their tractability. Through the lens of index-mixing, one can see the limited range of concordance of EFGM copulas, while this is not the case for index-mixed copulas in general.