MANUFACTURING AI OS — VEXPLOR
Your factory still
doesn't have an OS.
PCs have Windows. Smartphones have iOS. Your factory has none.
₩4.8B
Contracts secured (2026)
₩1.4B
2025 revenue
1
Gov't autonomous factory program (in progress)
20
Industry domains covered
Why factory AI fails
Your factory runs 5 to 12 systems. Each works well. But they don't know each other.
This isn't an AI problem. It's the absence of an OS.
Like phones before smartphones — no messaging app, because there was no OS.
What happens when you install an OS in your factory
The secret: The OS already knows 311 relationships.
7 expert agents work like this in your factory
AI agents built from top experts in each field.
They share views, verify data, and reach a decision.
This is not a chatbot.
It's an AI team where domain expert agents collaborate in real-time.
A true AI OS beyond solutions
This isn't a chatbot on top of solutions. All data connects into one knowledge graph, so AI understands how systems relate.
Semantic Layer (Knowledge Integration)
20 solutions, 834 tables, and 311 business relationships woven into one knowledge graph (Neo4j).
Kinetic Layer (Real-time Execution)
CDC and outbox patterns sync in real time. Detect a quality issue and related work orders halt at once.
Dynamic Layer (Autonomous Evolution)
GDS algorithms and 5-path hybrid RAG surface hidden patterns and tune weights from execution feedback.
Already in production
At the project site of the government autonomous-factory program, VEXPLOR's AI directly controls PLCs. The 85% digital-twin accuracy was confirmed in the on-site demonstration.
SpiPox Co., Ltd.
- Electronic-component materials · aluminum capacitor cases
- MES · Digital Twin · AI autonomous control · AAS · Edge
TOPSEAL Co., Ltd.
- MES build (large–SME collaboration program)
- Output 306→383 EA/HR · defect rate 2.3→1.7%
What's inside Factory OS
Just as iOS comes with Camera, Maps, and Messages built in,
Factory OS comes with everything built in for factory operations.
No-Code Canvas
Design tables on the canvas and the database, APIs, and screens are generated without code.
- Drag & drop schema design
- 6-stage auto-deployment (DDL → UI)
- 6 React components per table, auto-generated
- One-click solution templates
Manufacturing AI (Ontology-based)
A manufacturing-specific AI trained on 311 business relationships across 20 industrial domains.
- 5-path RAG (Graph·Vector·SQL·Doc + GoT)
- Answers cross-checked against the ontology
- Cross-domain auto-search (5 domains, 30s)
- AI maturity L1~L4 progressive evolution
Cross-Solution Ontology
The OS core: A knowledge structure that understands inter-system relationships.
- 1 item master → Auto-connects to 14 solutions
- Equipment failure → production, delivery, material impact
- 311+ relationships · 834+ tables · 145+ workflows
- 3-layer ontology (common → industry → auto-learning)
"Buying a solution" vs.
"Installing an OS"
Phones existed before the iPhone. But a phone on iOS was an entirely different thing.
The same is true for a factory on Factory OS.
Apps on the OS — Install only what you need
Factory OS comes with apps for 20 industrial domains ready.
Turn on what you need; the OS connects the rest.
ERP
Resource MgmtItem master · BOM · Sales/Purchase orders · Revenue/Costs · Finance · Pricing
Item master → hub for 14 domains / Orders - production plans (APS) / Costs - actual costs (MES)
Run your business on verified global standards
Every VEXPLOR industry solution complies with essential international standards and specifications at the core data model level by default.
ERP (Enterprise Resource Planning)
Integrates and manages all business processes including production, logistics, finance, accounting, and sales. VEXPLOR ERP supports real-time decision-making through multi-layered data connections.
MES (Manufacturing Execution System)
Tracks and controls all manufacturing floor activities in real-time, from production planning and work orders to process management and performance recording.
APS (Advanced Planning & Scheduling)
Simulates and optimizes production and material requirement plans under constrained resources and delivery conditions.
PLM (Product Lifecycle Management)
Manages data and processes across the entire product lifecycle from planning, design, manufacturing, maintenance, to disposal.
QMS (Quality Management System)
Systematically manages specification control, inspection criteria, nonconformance, and corrective actions to continuously improve product and service quality.
SPC (Statistical Process Control)
Analyzes and monitors manufacturing process data using statistical techniques to identify process anomalies early and support continuous improvement.
EAM (Enterprise Asset Management)
Manages equipment lifecycle through preventive and predictive maintenance, maximizing uptime and preventing unexpected downtime.
FEMS (Factory Energy Management System)
Monitors and analyzes energy consumption of key factory equipment in real-time to maximize energy efficiency and reduce carbon emissions.
Digital Twin
Creates a perfect virtual replica of physical manufacturing sites and equipment for real-time simulation and data visualization.
SCM (Supply Chain Management)
Optimizes the entire supply chain network from material procurement to final product delivery, reducing inventory costs and maximizing on-time delivery rates.
WMS (Warehouse Management System)
Controls all logistics flows within the warehouse from receiving to shipping, optimizing and managing inventory location and quantity in real-time.
TMS (Transportation Management System)
Integrates fleet dispatching, route optimization, real-time shipment tracking, and freight settlement to enhance logistics visibility and efficiency.
MRO (Maintenance, Repair & Operations)
Unifies demand planning, procurement, and inventory management for all indirect materials that indirectly support production activities.
CRM (Customer Relationship Management)
Integrates the entire customer experience from sales opportunity discovery to orders, contracts, and customer service to maximize revenue.
HR (Human Resource Management)
Digitizes core HR functions including recruitment, performance evaluation, compensation, and training to maximize organizational talent capability and performance.
Groupware (Collaboration)
Supports enterprise-wide communication through electronic approval, email, messaging, and calendar sharing to build a smart work environment.
ESG (Environmental, Social & Governance)
Systematically measures and reports corporate greenhouse gas emissions, supply chain ethics, and compliance data to support sustainable management.
ISMS (Information Security Management)
Comprehensively manages policy establishment, control implementation, and security audits to protect critical corporate information and data assets.
LIMS (Laboratory Information Management System)
Manages sample tracking, instrument data integration, test procedures, and results in laboratories to ensure quality data integrity and reliability.
HACCP (Hazard Analysis Critical Control Points)
A system that analyzes potential hazards in food manufacturing processes, establishes critical control points, and ensures safe food production.
CGMP (Current Good Manufacturing Practice)
An environmental control and procedure management system that ensures quality, safety, and efficacy throughout cosmetics manufacturing, packaging, and storage processes.
AI Personal Assistant Growth Model
From day one, your own AI personal assistant starts working. It grows with your factory.
Assistant Deploy
Each employee gets an AI assistant
Proactive Alerts
Detects anomalies and alerts you first
Plan + Approve
It plans, you approve, it executes
Full Operations
Supervisor agent orchestrates the factory
Start at Level 1.
As the OS learns your factory, it naturally evolves to Level 2, 3, 4.
Each employee gets their own AI personal assistant
40+ experts and 42 tools stand by, with the Supervisor Agent orchestrating.
Supervisor Agent
Sees the whole factory and coordinates assistants
Purchasing Assistant
Orders, supplier evaluation, pricing optimization
Quality Assistant
SPC monitoring, CAPA, inspection decisions
Production Assistant
Work orders, shift briefings, line balancing
Equipment Assistant
Predictive maintenance, health scoring
Works the same in a 30-person factory
A purchasing + quality dual-role gets one assistant that learns both jobs — from conversation, not job titles.
AI answers "What if?" with hard numbers
Test in a virtual factory without touching the real one.
Decisions run on data, not gut feeling.
"There is a correlation between temperature and defects"
"Raise mold temp by 5° and defect rate drops to 0.3%"
"If CNC Machine 3 breaks down, what's the impact?"
Test in the virtual factory first — zero risk to your real operations
AI proposes. Humans decide.
"What if AI acts on its own?" — five safety levels.
View
Shows data only. Makes no decisions.
"Current OEE is 87%"
Suggest
Analyzes and recommends. Never executes.
"Spindle replacement recommended. Reason: ..."
Approved Execution
Executes only after approval.
"PO created → Approval requested → Approved → Executed"
Auto Execution
Auto-executes only within predefined rules.
"Below safety stock → Auto-order (within limits)"
Emergency Response
Immediate action for safety/quality emergencies + full alert.
"Consecutive safety defects detected → Line stop + alert"
"AI proposes. Humans decide. Always."
L2 and above always require human approval. All AI actions are recorded in audit logs.
In your industry,
this is how it works
Automotive Parts (OEM Supply)
"IATF 16949 audit prep — let the OS handle it instead of Excel"
Food/Pharma (HACCP · GMP · CGMP)
"Recall scope assessment: 4 hours → 1 minute. Let the OS handle regulatory audits"
Chemicals/Materials (Batch Process)
"The OS tracks every variable in your batch recipes"
Semiconductor/Electronics
"Don't open 5 systems for yield drop root cause. Ask the OS"
Energy
"The OS auto-traces equipment→energy→product→emissions paths"
Why your factory needs
Ontology and Knowledge Graphs right now
A question for C-suite executives and plant directors:
Does the AI you're evaluating truly understand your 'shop floor context'?
Bad data must not become bad decisions
Put a probability-based chatbot straight onto the shop floor and you get catastrophic hallucinations.
VEXPLOR defines every factory fact (items, processes, defects) as an Ontology. AI decides and acts only within verified manufacturing rules.
"What's the impact of this equipment failure on order delivery?"
RDBMS and spreadsheets only pile up data — they can't trace meaning and context like "Equipment A fails → Process C delays → Customer E delivery slips."
VEXPLOR's Knowledge Graph links 834 tables and 311+ relationships into one network, traversing 10 steps in a second to find root causes.
No need to replace existing systems (Augment, Not Replace)
No need to discard stable systems (PostgreSQL, etc.). VEXPLOR adds a knowledge graph engine (Neo4j) for analytics and reasoning on top, synced instantly via CDC.
Frequently Asked Questions
Do I need to replace existing MES/ERP?
No. It adds a knowledge layer on top of your existing systems — same data, plus the context AI needs.
How long does deployment take?
Start immediately with the no-code canvas. Core-domain PoC takes 4 weeks; the full ontology build, 4 months.
Are there templates for my industry?
Templates are ready for automotive parts, chemicals/materials, food/pharma, and semiconductor/electronics — applied in one click.
What if AI makes wrong decisions?
The ontology is the guardrail. AI works only within defined business relationships, not guesses. "Inspection criteria for this item" is fact.
How is data security handled?
Full tenant isolation (separate DB schemas), JWT authentication, gateway-level enforcement. Customer data is never shared.
Is both cloud and on-premise available?
Yes. Both SaaS and on-premise, tailored to high-security manufacturing sites.
Factory OS Subscription Plans
Start with what you need. As the OS grows, AI grows with it.
Standard
For department-level adoption in a single plant
- AI levelLevel 1 · Q&A
- SolutionsSelective install
- OntologyBasic relationships
- AI chatBasic
- WorkflowsBasic
- No-code canvas
- Auto deploy
Professional
For full-plant system integration
- AI levelLevel 2 · Proactive alerts
- SolutionsAll 20
- Ontology311+ relationships
- AI chatAdvanced
- Workflows145+
- No-code canvas
- Auto deploy
Enterprise
For multi-plant and custom app development
- AI levelLevel 3 · Autonomous decisions
- Solutions20 + Custom
- Ontology+ Dynamic Layer
- AI chatAutonomous
- WorkflowsCustom
- No-code canvas
- Auto deploy
Everything at a glance — Comprehensive Documentation
Complex manufacturing systems, made approachable. Documentation anyone can understand and apply.
GET STARTED
Is your factory
ready to install an OS?
7 AI experts are ready to run your factory.
See expert agents working with your real data in a 30-minute demo.