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Many science domains need to build computationally efficient and accurate representations of high fidelity, computationally expensive simulations known as reduced order models. This paper presents the design and implementation of a novel reduced-order model (ROM) builder, the REVEAL toolset. This toolset generates ROMs based on science- and engineering-domain specific simulations executed on high performance computing (HPC) platforms. The toolset encompasses a range of sampling and regression methods for ROM generation, automatically quantifies the ROM accuracy, and supports an iterative approach to improve ROM accuracy. REVEAL is designed to be extensible for any simulator that has published input and output formats. It also defines programmatic interfaces to include new sampling and regression techniques so that users can &#x2018;mix and match&#x2019; mathematical techniques best suited to their model characteristics. In this paper, we describe the architecture of REVEAL and demonstrate its usage with a computational fluid dynamics model used in carbon capture.