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Deviations from the approved design or processes during mass production canlead to unforeseen risks. However, these changes are sometimes necessary due tochanges in the product design characteristics or an adaptation in themanufacturing process. A major challenge is to identify these risks early inthe workflow so that failures leading to warranty claims can be avoided. Wedeveloped Fountain as a contextual assistant integrated in the deviationmanagement workflow that helps in identifying the risks based on thedescription of the existing design and process criteria and the proposeddeviation. In the manufacturing context, it is important that the assistantprovides recommendations that are explainable and consistent. We achieve thisthrough a combination of the following two components 1) language modelsfinetuned for domain specific semantic similarity and, 2) knowledgerepresentation in the form of a property graph derived from the bill ofmaterials, Failure Modes and Effect Analysis (FMEA) and prior failures reportedby customers. Here, we present the nuances of selecting and adapting pretrainedlanguage models for an engineering domain, continuous model updates based onuser interaction with the contextual assistant and creating the causal chainfor explainable recommendations based on the knowledge representation.Additionally, we demonstrate that the model adaptation is feasible usingmoderate computational infrastructure already available to most engineeringteams in manufacturing organizations and inference can be performed on standardCPU only instances for integration with existing applications making thesemethods easily deployable.

Fountain -- an intelligent contextual assistant combining knowledge representation and language models for manufacturing risk identification