Training Manual — Section 34

AI System Technical Specification

The complete technical architecture of the NOSK Life AI ecosystem — models, workflows, data pipelines, decision logic, integration layers, and future AI evolution.

AI System Overview

Core Purpose

Provide accurate, fast, garment-specific AI assistance across garment technology, QC engineering, fabric & testing, production & efficiency, SOP guidance, factory improvement, sustainability, and compliance.

System Components

AI AssistantsAI Decision EngineGGKI Knowledge GraphData PipelineModel LayerIntegration LayerAnalytics Layer

AI Assistants (Functional Layer)

Garment AI

Construction guidance
Fit analysis
Measurement checking
Technical package interpretation

QC AI

Defect identification
Root cause analysis
Corrective actions
Preventive actions

Fabric AI

Fabric performance analysis
Testing interpretation
Shrinkage prediction
Fabric defect classification

SOP AI

Step-by-step SOP guidance
SOP validation
SOP optimization

Defect AI

Defect classification
Severity scoring
Pattern recognition
Defect heatmap generation

AI Decision Engine (Logic Layer)

Decision Logic Components

Rule-based logic
Pattern recognition
Knowledge graph traversal
Multi-step reasoning
Contextual inference
SOP mapping
QC decision trees

Example Decision Flow

1User query → classify category
2Retrieve GGKI nodes
3Apply decision rules
4Validate with QC logic
5Generate structured output
6Provide corrective/preventive actions

GGKI Knowledge Graph (Data Layer)

Graph Nodes

Garment types
Fabric types
Defects
Testing standards
SOPs
QC processes
Buyer requirements
Construction elements

Graph Relationships

Defect → Root Cause
Fabric → Testing Requirement
Garment → Measurement Point
SOP → Step Sequence
Buyer → Compliance Rule

Graph Strength — context-aware, structured knowledge retrieval

AI Model Layer (ML Layer)

Model Types

NLP models
Vision models (future)
Classification models
Prediction models
Recommendation models

Model Capabilities

Text interpretation
Defect classification
Measurement validation
Testing interpretation
Production prediction

Training Sources

GGKI
SOP library
Defect library
Testing standards
Factory datasets
Buyer requirements

Data Pipeline (Processing Layer)

Pipeline Stages

1Input parsing
2Category classification
3Knowledge retrieval
4Decision logic application
5Output structuring
6Feedback loop
7Model improvement

Data Types

Text queries
SOP documents
QC reports
Testing results
Production data
Fabric data

Integration Layer (API Layer)

Integrations

Web app
Mobile app
Enterprise dashboards
QC systems
Testing labs
Buyer portals

API Types

Knowledge API
AI decision API
QC analytics API
Testing API
Factory performance API

AI Output Structure (Response Layer)

Standard Output Format

AI Reliability & Quality Controls

Controls

Human-in-the-loop review
Knowledge validation
Model accuracy monitoring
Error detection
Bias prevention
Version control

AI KPIs

Accuracy
Consistency
Response time
User satisfaction
Factory improvement impact

AI Security & Compliance

Security Measures

Encrypted data flow
Secure API access
Role-based permissions
Factory data isolation

Compliance Areas

Testing standards
QC standards
Sustainability standards
Buyer requirements

AI Factory Automation (Future Phase)

Automated QC scoring

Defect prediction

Production forecasting

Smart testing alerts

AI-driven line balancing

Digital twin factory

Goal — move factories toward AI-powered smart manufacturing.

AI Evolution Roadmap (2026–2036)

Phase 1

AI assistants + GGKI integration

Phase 2

QC automation + defect prediction

Phase 3

AI factory dashboards + smart testing

Phase 4

AI digital twin + predictive QC

Phase 5

Fully autonomous AI garment factory ecosystem

The AI ecosystem in action

Explore the AI workflows and GGKI knowledge graph that power this technical specification.

AI workflows GGKI index