Production systems call for analysis techniques yielding reliable diagnostic and prognostic insights in a timely fashion. To this end, numerous reasoning techniques have been exploited, mainly within the simulation and formal verification realms. However, the technological barrier between these approaches and the target end users remains a stumbling block to their effective adoption. This paper presents a framework interposing a natural language-based interface between the interpretation of the user’s request and the reasoning tools. The user’s natural language request is automatically translated into a machine-readable problem. The latter is then dispatched to a proper reasoning engine and either solved through a simulation or a formal verification task, thus enabling a multi-perspective analysis of the production system and certifying the correctness and transparency of the obtained solutions. The outcome is then reprocessed to be human-interpretable. State-of-the-art Large Language Models (LLMs), with their robust capability to interpret the inherent ambiguity of natural language, perform both translations. We evaluate the framework on a lab-scale case study replicating a real production system. The results of the experiments suggest that LLMs are promising complements to derive insights from faithful reasoning engines, supporting accurate analysis.