We establish the existence of a broad class of asymptotically Euclidean solutions to Einstein's constraint equations whose asymptotic behavior at infinity is a priori prescribed. The seed-to-solution method (as we call it) proposed in this paper encompasses vacuum spaces as well as spaces with (possibly slowly decaying) matter, and generates a Riemannian manifold from any seed data set consisting of (1): a Riemannian metric and a symmetric two-tensor prescribed on a manifold with finitely many asymptotically Euclidean ends, and (2): a (density) field and a (momentum) vector field representing the matter content. We distinguish between several classes of seed data referred to as tame or strongly tame, depending whether the prescribed seed data provide a rough or accurate asymptotic Ansatz at infinity. We also encompass classes of metrics with the weakest possible decay (having infinite ADM mass) or with the strongest possible decay (i.e. having Schwarzschild behavior) at infinity. Our analysis is based on a linearization of the Einstein operator around a (strongly) tame seed data and is motivated by Carlotto and Schoen's pioneering work on the so-called localization problem for Einstein's vacuum equations. Dealing with Einstein manifolds with possibly very low decay and establishing estimates beyond the critical level of decay require a significantly different method. By working in a suitably weighted Lebesgue-H ölder framework adapted to the prescribed seed data, we analyze the nonlinear coupling taking place between the Hamiltonian and momentum constraints, and we study terms arising at the critical level of decay and, in this way, we uncover the novel notion of mass-momentum correctors. In addition, we estimate the difference between the seed data and the actual Einstein solution, which might of interest for numerical computation. Next, we introduce and study the asymptotic localization problem (as we call it), in which Carlotto-Schoen's localization property is required in an asymptotic sense only. By applying our method of analysis to a suitable parametrized family of seed data sets, we solve this problem at the critical decay level.