Generating initial stabilizing control policies that satisfy operational constraints in the absence of full model information remains an open but critical challenge. In this paper, we propose a systematic framework for constructing constraint enforcing initializing control policies for a class of nonlinear systems based on archival data. Specifically, we study systems for which we have linear components that are modeled and nonlinear components that are unmodeled, but satisfy a local Lipschitz condition. We employ kernel density estimation (KDE) to learn a local Lipschitz constant from data (with high probability), and compute a constraint enforcing control policy via matrix multipliers that utilizes the learned Lipschitz constant. We demonstrate the potential of our proposed methodology on a nonlinear system with an unmodeled local Lipschitz nonlinearity.