We are witnessing the emergence of decentralized AI pipelines wherein different organisations are involved in the different steps of the pipeline. In this paper, we introduce a comprehensive framework for verifiable provenance for decentralized AI pipelines with support for confidentiality concerns of the owners of data and model assets. Although some of the past works address different aspects of provenance, verifiability, and confidentiality, none of them address all the aspects under one uniform framework. We present an efficient and scalable approach for verifiable provenance for decentralized AI pipelines with support for confidentiality based on zero-knowledge proofs (ZKPs). Our work is of independent interest to the fields of verifiable computation (VC) and verifiable model inference. We present methods for basic computation primitives like read only memory access and operations on datasets that are an order of magnitude better than the state of the art. In the case of verifiable model inference, we again improve the state of the art for decision tree inference by an order of magnitude. We present an extensive experimental evaluation of our system.