In this paper we describe a paradigm for contentfocused matchmaking, based on a recently proposed model for constraint acquisition-and-satisfaction. Matchmaking agents are conceived as constraintbased solvers that interact with other, possibly human, agents (Clients or Customers). The Matchmaker provides potentiM 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. We also show empiricMly that this paradigm supports "suggestion strategies" for finding acceptable solutions more efficiently or for increasing the amount of information obtained from the Customer. This work also indicates some ways in which the tradeoff between these two metrics for evaluating performance can be handled.