Managing critical infrastructures such as drinking water networks poses a fundamental challenge in transforming vast streams of real-time data into actionable and accessible knowledge. The core operational issue lies in the cognitive burden placed on operators, who must manually correlate numerous disparate variables to answer complex questions, in a process that typically demands years of expert experience. This work addresses the democratisation of expert knowledge by introducing specialised conversational agents based on Large Language Models (LLMs). The proposed approach trains an assistant that learns from domain-specific data, transforming 156 real operational variables from a municipal system into a structured dataset comprising automatically generated technical conversations. The obtained results demonstrate that the specialised conversational agent acts as a senior engineer available 24/7, improving cognitive tasks with key gains: Situational Analysis (+35%), integrating operational, temporal, and environmental factors; Structured Differential Diagnosis (+18%), prioritising hypotheses with verification steps; and, Decision Optimisation (+22%), balancing conflicting criteria like cost and efficiency. The solution reduces reliance on specific personnel, making expertise accessible for proactive planning and effective decision-making by any worker.