We present a new matching approach for building balanced observational studies with respect to a particular individual. This individual can be a patient receiving treatment in a hospital, a student pursuing a given school degree, an ex-convict in a program of social re-integration. The approach first profiles the individual in terms of observed covariates; then it solves an integer programming problem in order to find the largest balanced sample that contains the individual close to the centroid of its convex hull. The integer program problem can be solved in large data sets quickly. The approach can be augmented with flexible machine learning regression methods, in the spirit double robust estimation. We discuss real and simulated data examples.