DX-AI Manufacturing Copilot is an integrated AI platform for manufacturers' digital transformation.
Overview
DX-AI Manufacturing Copilot provides customized strategies and solutions to support the effective introduction and utilization of AI technology in the manufacturing sector. It aims to create practical and sustainable value by gradually applying AI technology to the key value chains of manufacturing companies.
Strategic Approach: DX-AI utilizes two main strategic approaches to apply AI technology to manufacturing.
Approach
Value Chain-Based AI Application
AI is applied step-by-step to each stage of the manufacturing value chain (order, design, production, quality, finance) to realize process efficiency, quality control, and cost reduction through data-driven prediction and analysis.
Workflow Automation and AI Agents
Integrate manufacturing site data with n8n to create "context," and use AI agent frameworks like Dify to support decision-making, process optimization, and insight generation. In the initial stages, build rapid prototypes with proven platforms.
Value Chain-Based AI Application, Workflow Automation, and AI Agent Integration
AI Technology, Phased Implementation and Future Scalability, Expected Effects
Reasons for Choosing AI Technology and Platform
DX-AI Manufacturing Copilot utilizes n8n and Dify as core technologies.
n8n is a local workflow automation platform that seamlessly integrates databases, IoT and PLC equipment, document management, and communication channels to connect and automate various data in the manufacturing site.
Dify is an AI agent framework that provides various business values such as decision-making support, optimization analysis, and insight generation based on context data prepared through n8n.
Phased Implementation and Future Scalability
Initially, it starts with a minimal value chain scope and essential workflow automation and AI agent functions.
Detailed and sophisticated AI functions are gradually added through user and developer feedback, and the system aims for an open platform that is easy to apply new technologies by securing the flexibility and scalability of the system in line with the rapidly changing AI technology environment.
This phased approach lays the foundation for deeply understanding the needs of the manufacturing site and actively responding to changes.
Expected Effects
The introduction of DX-AI Manufacturing Copilot is expected to increase the efficiency of the manufacturing process and optimize quality control.
It contributes to cost reduction by supporting data-based real-time decision-making, and lays the foundation for strengthening competitiveness and entering the global market through the use of AI technology.
Ultimately, it will accelerate the digital transformation of manufacturing through continuous technology and process innovation.
AI Technology, Phased Implementation and Future Scalability, Expected Effects
Reasons for Choosing AI Technology and Platform
- n8n: A local workflow automation platform that seamlessly integrates databases, IoT devices, PLC equipment, document management, and communication channels. It easily connects and automates various data in the manufacturing field, providing a foundation for AI application.
- Dify: An AI agent framework that provides various business values such as decision-making support, optimization analysis, and insight generation based on pre-prepared context data through workflows.
Phased Implementation and Future Scalability
- In the initial phase, start with a minimal value chain scope and minimal functionality of workflow automation and AI agents, and gradually add detailed and sophisticated AI functions through user and developer feedback.
- As technology advances rapidly and new AI technologies continue to emerge, we aim for an open platform that ensures the flexibility and scalability of the system and facilitates the application of new technologies.
- Through a step-by-step approach, we deeply understand the needs of the manufacturing site and lay the foundation for actively responding to internal and external changes.
Expected Effects
- Increased efficiency in manufacturing processes and optimized quality control
- Data-driven real-time decision-making support and cost reduction
- Strengthening competitiveness through the use of AI technology and laying the foundation for entering the global market
- Accelerating the digital transformation of manufacturing through continuous technology and process innovation
Technology Stack
Composed of Next.js 15 + React 19 Frontend, FastAPI Backend, AI Module v2.0, and Dify AI Platform integration, it provides more intelligent manufacturing solutions through advanced AI agents based on LangChain/LangGraph and automated AI service generation.
Module Configuration
It is an enterprise-level manufacturing solution that provides a total of 15 specialized functions through 9 currently implemented modules and 6 IoT/GenAI modules planned for future development.
🌟 Key Features
🚀 Next.js 15 + React 19
Latest frontend stack based on Turbopack
⚡ FastAPI + Python 3.11+
High-performance asynchronous backend API
🤖 AI Module v2.0
Intelligent AI agent system based on LangChain/LangGraph
🎯 Dify AI Platform
Automated AI agent creation and management system
🔧 n8n Workflow
Visual automation workflow system
🎨 Complete Theme System
Dark/Light theme based on CSS variables
🔧 Integrated Icon System
42 SVG icons based on Lucide Icons
📊 Real-time Data Processing
Asynchronous streaming and real-time updates
🔒 Type Safety
Minimized runtime errors with full TypeScript integration
🆕 Key New Features (v2.0)
🎯 Dify AI Platform Integration
- Fully Automated AI Agent Creation: One-click agent creation based on templates
- Console API Integration: Full automation via Dify Console API
- Fallback Mode: Automatic switch to manual mode when API key is not set
- Various Agent Templates: Provides AI agent templates specializing in manufacturing
🔧 Independent Service Architecture
- Modularized Docker Service: n8n, Dify each managed independently
- Integrated Start Script: Batch start all services with unknown link
- Service-specific Health Check: Independently monitor the status of each service
🤖 AI Module v2.0 Fully Implemented
- Full LangChain/LangGraph Support: Fully utilize the latest AI framework
- Context Engineering v2.0: Question type analysis and service-specific specialization
- Service-specific Optimized Client: Optimized for chatbots, classification, and reports
- Asynchronous Processing: Background processing of large-capacity AI tasks
🌟 Key Features
🔬 Data Generation
Simulation-Based Data Generation
Data generation that mimics the actual manufacturing environment
Experimental Data Simulation
Automatic generation of DOE-based experimental data
Cost Data Generation
Production cost simulation data
Various Distribution Support
Normal distribution, uniform distribution, log-normal distribution, etc.
📊 Experimental Design
DOE (Design of Experiments)
Systematic experimental design
Various experimental methods
Full Factorial, Fractional Factorial, Latin Hypercube
Statistical analysis
ANOVA, regression analysis, response surface methodology
AI-based experimental design
Intelligent experimental design based on LangGraph
🤖 Product Data Analysis and Modeling
Machine Learning Model
XGBoost 3.0, CatBoost 1.2, Random Forest, Neural Network
Model Ensemble
Improve prediction accuracy by combining multiple models
Hyperparameter Optimization
Automatic tuning based on Bayesian Optimization
Real-time Prediction
Quality prediction and notification during production
⚡ Process Analysis
1
Real-time Monitoring
Real-time status tracking of production processes
2
Anomaly Detection
Statistical methods and ML-based outlier detection
3
Performance Indicator Analysis
OEE, yield, and quality indicator analysis
4
Equipment Status Monitoring
Tracking performance and status by equipment
💰 Cost Management
Cost Optimization
Optimization algorithm to minimize production costs
Cost Prediction
Cost prediction based on time series analysis
ROI Analysis
Return on investment analysis and scenario planning
Cost Structure Analysis
Detailed analysis of cost components
📑 AI Report
1
LangGraph Workflow
Fully implemented complex report generation pipeline
2
Automatic Report Generation
Automatic generation of LLM-based analysis reports
3
Context Enhancement
Quality improvement through Context Engineering
4
Various Format Support
Supports PDF, Excel, and PowerPoint formats
5
Asynchronous Processing
Large-capacity report background generation
🔄 Workflow
n8n Integration
Visual workflow editor integration
Automation
Automate and schedule repetitive tasks
API Integration
Data integration with external systems
Trigger-Based
Event-based automatic execution
🎯 AI Agent Management
Automated Agent Creation
Template-based one-click AI agent creation
Dify Platform Integration
Full automation via Console API
Manufacturing-Specific Templates
Professional AI agents for 9 domains
Fallback System
Automatic switch to manual mode when API key is not set
🔧 Dify Service Management
Multiple Service Management
Integrated management of multiple Dify instances
Real-time Connection Test
Real-time service status monitoring
App Creation and Management
Dify app lifecycle management
Manufacturing Use of n8n Workflow: Advantages, Opportunities, Scalability
Key Advantages of n8n Workflow
1
Visual Workflow Builder: Automation Without Code
Configure workflows with drag and drop without complex programming Intuitive node-based design: Each function is composed of independent nodes for modular development Real-time debugging: Visually track workflow execution
2
Supports 400+ Integrations
Manufacturing-specific integration: Supports industrial protocols such as MQTT, Modbus, OPC UA Cloud service integration: Major cloud platforms such as AWS, Azure, Google Cloud Database connection: Supports various DBs such as PostgreSQL, MySQL, MongoDB, InfluxDB
3
AI Native Platform
LangChain integration: Build AI agent workflows Vector database integration: Seamless integration with Weaviate, Pinecone, etc. LLM model support: Utilize various AI models such as OpenAI, Anthropic, local models
4
Enterprise-Grade Features
Self-hosting: Ensures data security and privacy Advanced permission management: SSO, role-based access control Scalability: Supports large-capacity data processing and high-performance workflows
n8n Utilization Opportunities in Manufacturing
1. Real-time Data Collection and Monitoring
// Example of manufacturing-data.json workflow for the current project
{
"Receiving Sensor Data": "Webhook → Data Analysis → Notification Generation",
"Threshold-Based Notifications": "Temperature > 200°C, Pressure > 3.0 → Immediate Notification",
"Batch Tracking": "Linking Quality Data Based on Lot ID"
}1
Facility Management and Predictive Maintenance
Facility Status Monitoring: Real-time vibration, temperature, and pressure data collection
Failure Prediction: Predicting equipment failure in advance by linking with ML models
Maintenance Scheduling: Automated maintenance notifications with conditional workflows
2
Quality Management Automation
Quality Data Collection: Automated collection of quality metrics for each production line
Anomaly Detection: Anomaly detection based on statistical methods and ML
Automated Quality Report Generation: AI-based quality analysis report generation
3
Supply Chain Optimization
Inventory Management: Real-time monitoring of inventory levels and automated ordering
Supplier Integration: Real-time integration with supplier systems via APIs
Delivery Tracking: Real-time tracking of delivery status through integration with logistics systems
Scalability in DX-AI Manufacturing Copilot
1. Currently Implemented Workflow Templates
Manufacturing Data Monitoring (manufacturing-data.json)
{
"센서 데이터 수신": "Webhook Endpoint",
"데이터 분석": "Threshold Check with JavaScript Function",
"알림 생성": "Conditional Notification System",
"응답 전송": "Real-time Feedback"
}System Monitoring (system-monitor.json)
{
"메트릭 수집": "CPU, Memory, Disk, Network Monitoring",
"상태 분석": "Threshold-based Health Assessment",
"알림 발송": "Multi-channel Notifications such as Email, Slack",
"보고서 생성": "System Status Summary Report"
}2. Future Expandable Workflow
A. IoT Sensor Integrated Workflow
// MQTT-based sensor data collection
MQTT Subscribe → Data Validation → Anomaly Detection → Notification Sending → Database Storage
B. AI-Based Prediction Workflow
// Real-time predictive modeling
Data Collection → Preprocessing → ML Model Prediction → Result Analysis → Decision Support
C. Quality Management Workflow
// Automatic quality inspection
Quality Data Collection → Statistical Analysis → Quality Indicator Calculation → Report Generation → Notification Sending
D. Cost Optimization Workflow
// Cost optimization automation
Cost Data Collection → Optimization Algorithm Execution → Cost Reduction Plan Proposal → Execution Plan Establishment
3. Advanced Integration Scenarios
A. Integration with Dify AI Platform
// AI agent-based decision making
Data collection → Dify AI analysis → Result interpretation → Automatic action execution
B. External System Integration
// ERP, MES system integration
Production data → n8n processing → ERP update → MES synchronization → Dashboard update
C. Multi-Channel Notification System
// Situation-specific notification routing
Event occurrence → Priority analysis → Appropriate channel selection → Notification sending → Response tracking
📊 Potential for Developing n8n Nodes Specialized for Manufacturing
Industrial Protocol Nodes
OPC UA Node: Industrial data collection standard
Modbus Node: Legacy equipment communication
Ethernet/IP Node: Allen-Bradley PLC communication
Manufacturing-Specific Analysis Nodes
OEE Calculation Node: Automatic calculation of Overall Equipment Effectiveness
SPC Node: Statistical Process Control
Six Sigma Node: Quality improvement analysis
AI/ML Integration Nodes
Prediction Model Node: Real-time prediction model execution
Anomaly Detection Node: ML-based outlier detection
Optimization Node: Production plan optimization
Implementation Roadmap
1
Phase 1: Basic Workflow Expansion
- IoT Sensor Integration: MQTT, Modbus protocol support
- Real-time Notification System: Multi-channel notifications (email, Slack, Telegram)
- Data Collection Automation: Automatic data collection from various sources
2
Phase 2: AI Integration
- Dify AI Platform Integration: AI agent-based decision making
- Predictive Model Integration: Real-time ML model execution
- Automated Report Generation: AI-based analysis reports
3
Phase 3: Advanced Automation
- External System Integration: ERP, MES, WMS system integration
- Advanced Analysis Workflow: Complex business logic automation
- Custom Node Development: Manufacturing-specific function implementation
The Value of n8n Workflow
As a core automation engine in the DX-AI Manufacturing Copilot, n8n Workflow provides the following values:
8+
Ready to Use
Immediately available with 8 templates
∞
Scalability
Infinitely expandable by developing manufacturing-specific nodes
100%
AI Integration
Perfect integration with Dify Platform
↑ROI
Cost Efficiency
High ROI based on open source
This enables the implementation of smart factories and accelerates digital transformation, directly contributing to increased productivity and improved quality in manufacturing.
🤖 Key Advantages of Dify AI Agent
1
Production-Ready Platform
Complete AI Workflow: One-click deployment from prototype to production
107,000+ GitHub Stars: Industry-proven, stable platform
Enterprise-Grade Features: Complete with enterprise features such as SSO, permission management, and monitoring
2
Support for Various AI Models
3
Key Features of Agent Functionality
LLM Function Calling: Accurate task execution through structured function calls
ReAct Pattern: Iterative execution of Reasoning and Action
50+ Built-in Tools: Google Search, DALL·E, Stable Diffusion, WolframAlpha, etc.
Custom Tool Development: Development and integration of manufacturing-specific tools possible
4
RAG Pipeline Integration
Document Collection: Supports various formats such as PDF, PPT, etc.
Vector Database: Seamless integration with Weaviate, Pinecone, etc.
Intelligent Search: Context-based accurate information retrieval
🏭 Dify AI Agent Utilization Opportunities in Manufacturing
1. Manufacturing Process Optimization Agent
// manufacturing_consultant template for current project
{
"Areas of Expertise": [
"Manufacturing process optimization (productivity improvement, process improvement)",
"Quality control (QC/QA, defect rate reduction, quality system)",
"Safety management (industrial safety, risk assessment, safety training)",
"Equipment management (preventive maintenance, failure diagnosis, equipment selection)",
"Production planning (scheduling, inventory management, delivery management)"
],
"Response Method": [
"Provide specific and actionable solutions",
"Mention relevant laws or standards if available",
"Always emphasize safety-related matters",
"Provide step-by-step guidance if necessary"
]
}1. Real-time Monitoring and Notification Agent
Equipment Condition Monitoring: Real-time sensor data analysis and anomaly detection
Quality Control Automation: Real-time tracking of quality indicators and suggestions for improvement
Safety Management: Automatic detection of risk factors and establishment of response plans
2. Predictive Maintenance Agent
Failure Prediction: Predict equipment failures in advance by linking with ML models
Maintenance Scheduling: Establish data-driven optimal maintenance plans
Part Lifespan Prediction: Analyze lifespan cycles for each part and predict replacement timing
3. Quality Control Expert Agent
Quality Data Analysis: Real-time quality indicator analysis and trend identification
Defect Cause Analysis: Diagnose defect causes through statistical methods and ML
Improvement Plan Suggestion: Establish data-driven quality improvement strategies
🚀 Scalability in DX-AI Manufacturing Copilot
1. Currently Implemented AI Agent System
Automated Agent Creation System
# Core functionality of backend/routers/dify_agent.py
@router.post("/agents/auto-create/{template_id}")
async def auto_create_agent_from_template(
template_id: str,
name: str,
description: str = ""
):
# 1. Automatically create a Dify app
# 2. Configure app settings (model, prompt, tools)
# 3. Automatically generate API key
# 4. Register local agentManufacturing-Specific Templates
// Currently implemented 9 domain-specific expert AI agents
{
"manufacturing_consultant": "Manufacturing Consultant Bot",
"quality_analyst": "Quality Analysis Expert",
"safety_manager": "Safety Management Expert",
"equipment_engineer": "Equipment Engineer",
"cost_optimizer": "Cost Optimization Expert",
"process_analyst": "Process Analysis Expert",
"maintenance_planner": "Maintenance Planner",
"inventory_manager": "Inventory Management Expert",
"production_scheduler": "Production Planning Expert"
}2. Future Expandable AI Agents
A. IoT Data Analysis Agent
// Real-time sensor data analysis
{ "기능": [ "Real-time sensor data collection and analysis", "Automatic detection of abnormal patterns", "Failure prediction through predictive modeling", "Equipment performance optimization suggestions" ], "도구": [ "MQTT connection tool", "Time series analysis tool", "ML model execution tool", "Notification sending tool" ] }
B. Quality Control Automation Agent
// Automation based on quality data
{ "기능": [ "Real-time monitoring of quality indicators", "SPC (Statistical Process Control) automation", "Automatic analysis of defect causes", "Automatic presentation of quality improvement measures" ], "도구": [ "Statistical analysis tool", "Visualization tool", "Report generation tool", "Notification system tool" ] }
C. Cost Optimization Agent
// Cost optimization expert agent
{ "기능": [ "Real-time analysis of production costs", "Calculation of optimal input", "Presentation of cost reduction measures", "ROI analysis and prediction" ], "도구": [ "Optimization algorithm tool", "Simulation tool", "Cost analysis tool", "Report generation tool" ] }
3. Advanced Integration Scenarios
A. Multi-Agent Collaboration System
// Collaboration of multiple AI agents
{ "Scenario": "Production line anomaly", "Agent Collaboration": [ "Monitoring Agent: Anomaly detection and notification", "Diagnosis Agent: Root cause analysis and diagnosis", "Solution Agent: Solution proposal", "Execution Agent: Automatic action execution" ] }
B. Integration with n8n Workflow
// Dify AI Agent + n8n Workflow
{ "AI Agent": "Decision making and analysis", "n8n Workflow": "Automated task execution", "Integration Scenario": [ "AI agent presents analysis results", "n8n executes automated actions", "AI agent re-evaluates results", "Continuous optimization cycle" ] }
C. Real-time Dashboard Integration
// Real-time monitoring dashboard
{ "AI Agent": "Data analysis and insight provision", "Dashboard": "Real-time visualization and notification", "Integration Features": [ "Real-time data streaming", "AI-based predictive visualization", "Interactive analysis tools", "Automated report generation" ] }
📊 Potential for Developing Dify Tools Specialized for Manufacturing
Industrial Protocol Tools
- OPC UA Tool: Industrial data collection standard
- Modbus Tool: Legacy equipment communication
- MQTT Tool: IoT sensor data collection
Manufacturing Analysis Tools
- OEE Calculation Tool: Automatic calculation of Overall Equipment Effectiveness
- SPC Tool: Statistical Process Control
- Six Sigma Tool: Quality improvement analysis
AI/ML Integration Tools
- Prediction Model Tool: Real-time prediction model execution
- Anomaly Detection Tool: ML-based outlier detection
- Optimization Tool: Production plan optimization
Implementation Roadmap
Phase 1: Basic AI Agent Expansion
- IoT Data Analysis Agent: Real-time sensor data analysis
- Quality Management Agent: Automatic quality analysis and improvement plan
- Maintenance Prediction Agent: ML-based predictive maintenance
Phase 2: Advanced AI Agent
- Multi-Agent Collaboration: Collaboration system for multiple AI agents
- Custom Tool Development: Development of manufacturing-specific tools
- Real-time Dashboard Integration: AI agent and dashboard integration
Phase 3: Full Automation
- Autonomous Decision-Making System: AI agent-based automatic decision-making
- External System Integration: Integration with ERP, MES, WMS systems
- Advanced Analysis Workflow: Automation of complex business logic
Value of Dify AI Agent
The Dify AI Agent serves as a core AI engine in the DX-AI Manufacturing Copilot, providing the following values:
9+
Ready to use
Immediately usable with 9 templates currently available
∞
Scalability
Infinitely scalable through the development of manufacturing-specific tools
100%
Automation
Perfect integration with n8n workflows
↑ROI
Cost efficiency
High ROI based on open source
4.0
Future-oriented
Preparing for Industry 4.0 and AI-First manufacturing
This enables the realization of a smart factory and accelerates digital transformation, directly contributing to increased productivity and improved quality in manufacturing. In particular, it can drive innovation in manufacturing through automated decision-making and real-time optimization.
Real-time Information-Based LLM Inference and Manufacturing Site UX Innovation Through Context Engineering
🧠 Core Values of Context Engineering
1. Real-time Situation Awareness Context
# Currently Implemented Context Engineering System
class ContextEngineer:
def create_situation_context(self, params: Dict[str, Any], topic: str) -> str:
"""Creates situation awareness context by topic"""
if topic == "experimental_design":
return self._create_experimental_design_context(params)
elif topic == "process_analysis":
return self._create_process_analysis_context(params)
elif topic == "cost_management":
return self._create_cost_management_context(params)
2. Real-time Data-Driven Context
// Passing Real-time Simulation Parameters
simulationParams={{
activeTab: activeTab,
hasData: !!data,
dataInfo: dataInfo,
uploadedFileName: uploadedFile?.name,
selectedColumns: selectedColumns,
correlationThreshold: correlationThreshold,
selectedColumn: selectedColumn,
chartType: chartType,
preprocessingCompleted: !!preprocessingResult,
isAnalyzing: isAnalyzing,
isPreprocessing: isPreprocessing
}}3. Dynamic Context Optimization
Topic-Specific Filtering
Extract only parameters specific to each function
Real-Time Status Reflection
Transmit the current task status to the LLM in real-time
User Behavior Patterns
Reflect the user's workflow in the context
Real-Time Information Utilization Opportunities in the Manufacturing Field
// Example of real-time data from the production site
{
"production_context": {
"current_lot": "LOT-2024-001",
"equipment_status": {
"reactor_01": "running",
"reactor_02": "maintenance",
"reactor_03": "idle"
},
"quality_metrics": {
"temperature": 185.5,
"pressure": 2.3,
"purity": 98.2,
"yield": 94.8
},
"alerts": [
"reactor_01_temperature_high",
"reactor_02_maintenance_due"
],
"production_schedule": {
"current_phase": "reaction",
"time_remaining": "2h 15m",
"next_phase": "cooling"
}
}
}2. Real-time Monitoring Context
// Real-time monitoring data
{
"monitoring_context": {
"sensor_data": {
"temperature_sensors": [185.5, 187.2, 183.8],
"pressure_sensors": [2.3, 2.4, 2.2],
"flow_rates": [150, 148, 152],
"vibration_levels": [0.2, 0.3, 0.1]
},
"anomaly_detection": {
"anomalies_detected": 2,
"severity_levels": ["medium", "low"],
"recommended_actions": [
"check_temperature_control",
"schedule_maintenance"
]
},
"performance_metrics": {
"oee": 87.5,
"uptime": 94.2,
"quality_rate": 96.8
}
}
}3. Real-time Context of Quality Management
// Real-time data for quality management
{
"quality_context": {
"current_batch": {
"batch_id": "BATCH-2024-001",
"sample_count": 150,
"defect_count": 3,
"defect_rate": 2.0,
"quality_score": 98.0
},
"quality_trends": {
"last_24h_defect_rate": 1.8,
"weekly_average": 2.1,
"monthly_trend": "improving"
},
"quality_alerts": [
"defect_rate_increasing",
"sample_quality_degrading"
]
}
}Implementation Roadmap
1
2
3
1
Phase 3: Full Automation
Autonomous Operation / Predictive Maintenance / Optimization Engine
2
Phase 2: Advanced AI Action System
AI-based Decision Making / Complex Action Workflow / Learning System
3
Phase 1: Real-time Context Engineering
Real-time Data Collection / Context Optimization / Basic Action Linking
Innovative Opportunities
1
Real-time Decision-Making Support
Instant Context Provision: LLM instantly understands the current situation
Predictive Insights: Predict future situations based on past data
Automated Actions: Execute automatic actions based on the situation
2
User Experience Innovation
Intuitive Interface: Provides complex data in an easy-to-understand format
Personalized Experience: Interface tailored to user roles and preferences
Real-time Feedback: Instantly see the results of actions
3
Productivity Improvement
Reduce Decision-Making Time: Support quick decisions with real-time information
Error Reduction: Prevent errors in advance with AI-based predictions
Increased Efficiency: Automated processing of repetitive tasks
The Value of Context Engineering
Real-time information-based LLM inference and UI-specific Chatbot Widgets through Context Engineering offer innovative opportunities to accelerate the digital transformation of the manufacturing site:
100%
Real-time situational awareness
Currently implemented Context Engineering combined with real-time data is immediately applicable
UX
Intuitive User Experience
Floating chatbot widgets provide complex manufacturing data in an easy-to-understand format
→
Connection to Real Actions
Provides concrete and actionable actions based on real-time context
1:1
Personalized Support
Customized AI support tailored to user roles and situations
This enables the realization of smart factory implementation and manufacturing innovation, and can directly contribute to increased productivity and improved quality in the manufacturing field.