LLM Digital Twins for Air Compressor Energy Optimization

Solution Description

ESG

By leveraging AI digital twin models, we predict KPI performance to support decision-making for backup machine selection, enabling energy savings and operational efficiency. Explainable AI is used to analyze the parameters influencing KPI, revealing that air compressor efficiency is strongly correlated with its shaft bearings, highlighting the importance of predictive maintenance for these components. Additionally, an intuitive dashboard visualizes the relationship between demand and supply data, facilitating real-time decision-making and responsiveness. With the integration of LLM Agents, users can communicate seamlessly with digital twins through natural language, enhancing usability and interaction efficiency.

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LLM-Based Decision Support System for Abnormal Customer Complaints

moraleai

128 0

AI / AIoT

LLM-Based Decision Support System for Abnormal Customer Complaints

Handling abnormal complaints requires extensive experience and expertise. However, the resignation or retirement of senior employees often leads to knowledge loss, impacting organizational efficiency and competitiveness. Through vector models, Morale AI transforms unstructured data (such as emails, PDFs, and Word documents) into structured data and integrates it into databases, enhancing data usability. Multi-modal models combine diverse data types, including text and images, to systematically record and preserve experience. By leveraging Retrieval-Augmented Generation (RAG) technology and similarity search, the model can accurately categorize issues, identify the root causes, and recommend relevant cases, significantly reducing manual processing time and error rates. This systematic approach not only enhances response efficiency and enables real-time analysis and resolution of customer issues but also reduces repetitive work for employees, improving overall productivity and customer satisfaction.

moraleai

139 0

AI / AIoT

TextileGPT

TextileGPT is an AI platform specifically designed for the textile industry, combining TNet's historical expertise and professional knowledge to accelerate product launches, reduce development costs, and strengthen knowledge transfer. Built on the TNet knowledge base, it provides intelligent dialogue capabilities enriched with industry-specific knowledge to address various challenges. The platform supports processing multiple data formats, including PDF, PPT, DOC, XLS, and web content, and features a user-friendly interface for quick adoption. TextileGPT aims to tackle issues like talent gaps and difficulties in knowledge inheritance, driving innovation and efficiency to enhance competitiveness, making it a key enabler for the textile industry's future development.