Knowing whether a COTS-AI model is believable for use in a deployment context is crucial. The main concerns are whether actual input distribution matches what was used for training, whether there are some error-prone regions of input space where the AI is prone to fail. Significantly less attention has been given to address such lacuna in deployment scenarios. To fill this gap, we propose a first-of-its-kind novel solution known as Believable AI. This is the first paper to provide a theoretically strong algorithmic solution that will enable the COTS-AI buyer to (i) characterize inputs for which the model should be believed, (ii) certify its usage as believable given the risk tolerance in a given context and (iii) provide evidence based on above characterization for believing the model’s output. The proposed solution is based on a statistical analysis of the model performance on labeled inputs and inter-sample distances. We show with a wide variety of network architectures and publicly available datasets how buyers of COTS-AI can benefit from these measures, how designer/developers of AI can make claims about their products without revealing design or complete data related IPR, and how a regulatory compliance mechanism can be built to make COTS-AI products believable to use.