Uncertainty arises in many compelling real-world applications of planning. There is a large body of work on propositional uncertainty where actions have non-deterministic outcomes. However handling numeric uncertainty has been given less consideration. In this paper, we present a novel offline policy-building approach for problems with numeric uncertainty. In particular, inspired by the planner PRP, we define a numeric constraint representation that captures only relevant numeric information, supporting a more compact policy representation. We also show how numeric dead ends can be generalised to avoid redundant search. Empirical results show we can substantially reduce the time taken to build a policy.