This paper presents research on the feasibility and development of methods for the rapid creation of stopgap language technology resources for low-density languages. The focus is on two broad strategies: (i) related language bootstrapping can be used to port existing technology from a resource-rich language to its associated lower-density variant; and (ii) clever use of linguistic knowledge can be employed to scale down the need for large amount of training or development data. Based on Persian and Armenian languages, the paper illustrates several methods that can be implemented in each instance in the goal of reducing human effort and avoiding the scarce data issue faced by statistical systems.