Neuro-symbolic AI for Industrial Configuration

Large Language Models (LLMs) have shown impressive performance on a wide range of generative tasks. Yet their probabilistic nature makes them, in isolation, fundamentally unsuited for industrial product configuration, where outputs must be syntactically valid, semantically consistent with a knowledge base of hundreds of features and rules, and producible by an existing manufacturing chain. We argue that Neuro-symbolic (NeSy) AI methods lay out a promising path towards industrial-grade configurators that are reliable by design, explainable, and trustworthy. This paper presents the taxonomy of three NeSy integration strategies, namely hybrid inference, hybrid fine-tuning, and hybrid training, describing each along the axes of architecture, source of reliability guarantees, and technology readiness. We report our effort to operationalize NeSy concepts in an industrial configuration-copilot prototype and derive a set of practical insights for deploying trustworthy AI in engineering environments. We close with a discussion of open research challenges we consider most pressing, in particular how to scale NeSy methods from small academic demonstrators to the size of industrial configurators.