Current methods to develop standard Architecture, Engineering, Construction (AEC) product models focus on the definition of product model semantics without concurrent and formal consideration of the engineering analyses that such models must support, or formal consideration of the requirements for sharing information between applications. We present two case studies that demonstrate a service to extract data from product models and provide inputs to component analysis applications. The service was validated in a proof-of-concept application called the Internet Broker for Engineering Services (IBES) that extracts information for component analysis from product models that are external to the application and accessed across the Internet. IBES was tested for two research cases. The product model for the first case, control valve selection is based on STEP Application Protocol 227. The product model for the second case, control valve diagnosis, specifies additional semantics that support the operations and maintenance (O&M) phase of the facility life cycle. The cases offer evidence that large standard data models can support routine analyses for control valves. However, the amount of shared information between the case applications is small and is largely dependent upon the concurrence of component behaviors that are necessary to model analysis. The IBES reference model and reasoning to support information extraction was consistent for both cases. This consistency suggests that it is possible to define a general set of computational methods that integrate project information models with external component analysis applications across the product life cycle. We argue that enabling a web-based link between product models and applications requires a set of capabilities, including bi-directional communication between separated data and analysis nodes, query generation, data translation, and validation of data extracted from semistandard models. We discuss the tentative implication that minimal shared information calls into question the assumption that large core product models will work effectively in practice.