  This paper describes and evaluates a multistage approach to AI deployment.
Each stage involves a more accurate method of inference, yet engaging each
comes with an increasing cost. In outlining the architecture, we present a
method for quantifying model uncertainty that facilitates confident deferral
decisions. The architecture is currently under active deployment to thousands
of cotton farmers across India. The broader idea however is applicable to a
growing sector of AI deployments in challenging low resources settings.
