We describe a paradigm for content-focused matchmaking, based on a recently proposed model for constraint acquisition-and-satisfaction. Matchmaking agents are conceived as constraint-based solvers that interact with other, possibly human, agents (Customers). The Matchmaker provides potential solutions ("suggestions") based on partial knowledge, while gaining further information about the problem itself from the other agent through the latter’s evaluation of these suggestions. The dialog between Matchmaker and Customer results in iterative improvement of solution quality, as demonstrated in simple simulations. This paradigm also supports "suggestion strategies" for finding acceptable solutions more efficiently or for increasing the amount of information obtained from the Customer.