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.

1. Directly driving fans with a motor increases transmission efficiency and highlights the superiority of direct drive systems over traditional transmission systems, especially when combined with a variable-speed control mechanism and at lower rotating speeds. 2. The absence of mechanical components in a direct drive system eliminates the need for regular maintenance of the reducer, gearbox, and transmission shaft, as well as reduces the risk of oil leaks. This results in lower maintenance costs, longer product life, and reduced operating costs for customers. 3. Installation of TECO's proprietary inverter for cooling tower can reduce operating noise, buffer activation, and lower mechanical impact. 4. A 30 kW motor in constant-frequency operation can reduce total operating costs by up to 18% over a five-year period.

RiChitech has pioneered integrating generative AI models with GIS drawing technology, developing the generative smart map drawing module—geoAI Maker. This module is designed for users with many spatial description documents and graphic information but cannot identify locations and create maps. geoAI Maker can automatically recognize text and images, parse various types of spatial descriptions (such as addresses, landmarks, and range information), and further convert the content into GIS spatial coordinates, automatically generating vector maps. This process fully leverages the powerful capabilities of generative AI, allowing users to immediately utilize advanced analysis and visualization displays, helping various industries solve and accelerate the processing of spatial data.

ITS traffic data collection has solved the laborious and time-consuming task of manually calculating traffic flow in the past. It aims to achieve the goals of smoother road conditions and reduced accidents through intelligent traffic diagnosis. By utilizing intelligent transportation systems, the system automatically tracks vehicles, identifies vehicle types, predicts trajectories, and transforms the data into useful information. This assists in real-time interpretation and improvement of traffic engineering. This system is particularly crucial in Taiwan, where over 60% of traffic accidents occur at intersections. Through big data analysis, identifying issues and formulating solutions is possible, thereby lowering accident risks. This approach proves more effective than simple advocacy and alerts.

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.

AI on-site Earthquake Early Warning System A highly optimized system setting and design to sense earthquake P-Wave within 3 sec and through AI model to predict PGA and PGV immediately and send information to customer for further application Product Advantage -Customization by customer demand -Professional team to optimize system set up to avoid environmental noise and interference -Multiple system design to enhance system stability and efficiency -Close loop design for information security -AI model with best system set up for best performance in speed and accuracy -Evolving AI model with more data collection

The accounting system will automatically send reminder bills via Line and Email. The payment account supports online cash flow and offline payment. If you use a bank account that cooperates with JGB, it will have a 24-hour automatic reconciliation function. The tenant will immediately remit the payment. Notification, say goodbye to the daily routine of Excel reconciliation.

AI Image Recognition (Intelligent Storage): Install surveillance cameras in the warehouse campus to accelerate the identification speed of AI Intelligent Image Analysis System through 5G network, and assist the management of the enterprise campus. AR Remote Equipment Maintenance: Use a 5G network to enable field workers to communicate with remote experts through AR smart glasses and simultaneously initiate multi-party video collaboration. Light A.I. Virtual Guard: Replace traditional human monitoring with AI cameras, connect to the cloud in 5G for real-time image recognition, and achieve real-time alerts via monitoring platforms and mobile app. AGV/AMR applications: Use 5G wireless connectivity technology to move cargo to designated picking stations and communicate with other equipment in the field to improve efficiency and reduce labor costs.

By applying environmental sensing IoT technology and mobile sensors, the coverage of sensing can be significantly enhanced. With the analysis through AI algorithms, it assists governments in identifying potential sources of pollution for regulatory actions and pollution reduction measures. Meanwhile, supplemented by generative AI innovation technology, it achieves smart environmental governance.

In order to achieve the goal of intelligent transportation in Hualien and alleviate the crowds and traffic problems brought about by the opening of the entire Suhuakai line and the traffic problems in the city center, the Hualien County Government has developed a design that conforms to the local characteristics and constructs intersection numbers through the "Development and Construction Design of Intelligent Transportation System". Using smart technology to make traffic policies more accurate. Entrusted "Chunghwa Telecom Co., Ltd." to stage software and hardware in the "2018 Hualien Intelligent Traffic Integration Management System Construction Plan in Response to Su Hua Changing Vehicles", "2019 Hualien County Smart Traffic Management System Expansion Plan", The "109-Year Hualien County Smart Parking and Guidance System Construction Plan", "110-Year Hualien County Smart Road Network Management Integration and Traffic Corridor Improvement Plan" and other projects have been completed successively. There are five functions for overall optimization and refinement: "Cloud Smart Traffic Control", "Restructuring Time Signs", "Traffic Big Data Value-Added Application", "Signal History", and "Smart Parking"

This project establishes a 5G private network in rural Tainan schools, introducing AR/VR gamified English teaching. Students engage in scenario-based games, simulating English conversations in virtual reality, fostering entertaining and interactive learning. The curriculum incorporates famous global landmarks and utilizes AR technology for checkpoint missions, enhancing student interest. Beyond English education, the initiative collaborates with the National Palace Museum, integrating AR/VR historical artifacts. Using 5G streaming, historical content is seamlessly integrated into campus teaching. The project not only enhances learning outcomes in rural junior high schools, reducing the urban-rural digital gap, but also has the potential for nationwide expansion. Through cross-disciplinary collaboration, it drives the development of both horizontal and vertical industry chains.

The Smart Dust Sensor technology is an innovative solution for integrating IoT networks into air quality monitoring. Developed collaboratively by the National Science and The National Science and Technology Council (NSTC), in collaboration with Taiwan Instrument Research Institute (TIRI) and Taiwan Semiconductor Research Institute (TSRI) of NARLabs, and leading academic teams in Taiwan, this technology has successfully miniaturized gas sensors while achieving low power consumption and high sensitivity. Its core features include exceptional sensitivity and stability, enabling real-time monitoring of multiple gases. Moreover, the fully domestically developed technology reduces dependence on imports and offers cost-effective sensor modules, driving the widespread adoption of air quality monitoring devices. The Smart Dust Sensor has undergone extensive field testing and third-party certifications across various applications. These include environmental monitoring stations (e.g., Taichung Dali Station), public recreational areas (e.g., odor monitoring in public restrooms), and industrial settings (e.g., semiconductor facilities and wastewater treatment plants). With its proven accuracy, reliability, and traceable data, the technology plays a key role in advancing air quality monitoring solutions, facilitating the development of smart cities, and contributing to improved environmental management.