Carbon Neutral and AI :
Unlock the door to Corporate Sustainability

Implement IoT carbon emission management and internal carbon pricing for companies, linking finance/accounting, ERP, and MES to calculate the carbon footprint and profits/losses of products. Establish a customized AI agent system for the company/factory to solve labor shortages and pass down domain knowledge!

Green Shield

Carbon MRV (IoT) Management System

International Supply Chains: Require Taiwanese manufacturers to provide carbon emissions data for specific product SKUs upon shipment.
CBAM (Carbon Border Adjustment Mechanism): Mandates quarterly reporting of product carbon emissions for goods exported to the European Union.
Financial Supervisory Commission (FSC): Requires publicly listed companies to align with IFRS Sustainability Disclosure Standards. This includes disclosing sustainability information (S1) in consolidated financial statements and climate-related information (S2).

Corporate Status Health Check

3-5 days

Equipment Networking Plan

7-14 days

Real-Time Report Validation

60 days

IoT Carbon Emission Management

Taiwan's traditional manufacturing industry, including supply chains and publicly listed companies, is facing the demands of international carbon emission regulations. Under the pressures of labor shortages and power shortages, it is crucial to first grasp the carbon emission data during the production process before managing work dispatch and production lines (OEE). This is necessary to implement the phased carbon reduction goals of the company.

The Green Shield GS 300 uses IoT/Internet of Things technology to attach IoT sensors to the existing equipment in factories. These sensors can record critical data in real-time, such as equipment temperature, humidity, production quantity, unit power consumption, unit carbon emissions, production yield, and machine utilization rate. This data provides a precise foundation for carbon inventories.

With production equipment data, companies can optimize production processes and adjust work dispatch, conduct production line weakness analysis, and plan phased upgrades to equipment and systems. This will improve production management efficiency and help alleviate the pressures of labor shortages and carbon emissions!

Enterprise-Specific GPT/Virtual Host

Large Language Models (LLMs) possess capabilities such as knowledge organization, multilingual translation, and question-answer generation. However, there is a concern that sensitive corporate data may leak through vulnerabilities in cloud-based applications.

We use Retrieval-Augmented Generation (RAG) technology to build an enterprise-specific knowledge graph vector database (Neo4J) that integrates information such as service processes, production procedures, corporate culture, and brand values. This system leverages the power of large language models while ensuring that inferences and responses are generated accurately and in a timely manner from the dedicated database. It is the best solution for addressing labor shortages, knowledge transfer, marketing, and customer service!

By combining IoT and Automatic Speech Recognition (ASR) technologies with large language models (LLMs), we create an autonomous agent system that acts as the enterprise’s brain. It can be presented as a virtual host (digital human) and interface with internal systems, helping businesses move toward intelligent operations!

In the wastewater treatment plant, we combined the image detection and recognition function of the camera with the large-scale linguistic model of the agent as the first management assistance system in Taiwan for daily operation and equipment maintenance + comfort monitoring in response to heavy rainfall.