Industry 4.0 enables industrial appliances to generate vast operational datasets, yet their interpretation often remains inaccessible to end-users. This study evaluates Large Language Models (LLMs) as "Interactive Engineering Assistants" to democratize the analysis of raw telemetry from refrigeration units, aligning with EU Data Act (2023/2854) transparency mandates. Data were extracted from the Smart Shop Control (SSC) ecosystem—a proprietary IIoT platform architected by the author, managing over 35,000 active devices. A research gap was addressed regarding the "zero-shot" interpretation of semi-structured CSV sensor data by models optimized for natural language. Two experiments utilized 2-hour telemetry segments in anonymized and overt formats to evaluate SOTA models (GPT-5.1, Copilot, Gemini) under a 'Stateless Human-Orchestrated Sequential Prompting' paradigm. Results demonstrate that LLMs autonomously identify thermodynamic anomalies (e.g., condenser fouling) and correlate them with physical phenomena, establishing a new 'Product Truth' standard. The study introduces the LLM as a 'Mirror of Competence', where diagnostic efficacy reflects the operator's engineering logic. Furthermore, integrating the Unconscious Waste Indicator (UWI) within LLM reasoning identifies hidden energy losses. Public LLM interfaces provide a practical 'Privacy-by-Anonymity' layer, democratizing industrial diagnostics for non-expert stakeholders.