FI
FactoryIQ
Intelligent Factory Platform · MES · PLM · QMS · CMMS
AI learning from 1,914 manufacturing outcomes
ENV: PROD · ROLE: QUALITY LEAD
Dashboard
TrendIQ 10
DecisionIQ 8
ActionIQ 8
EffectivenessIQ 24
KNOWLEDGE FOUNDATION
Ontology
Semantic Models
Knowledge Graph
ArchitectureIQ
Intelligent Factory Operations
Continuous quality, supplier, reliability and process intelligence across the plant network
◊ Knowledge graph: 48,217 linked artifacts
Quality Yield
96%
↑ 2 pts vs Q1
Trace Coverage
88%
lot → product linked
First-Pass Yield
91%
across lines
RCA Cycle Time
-22%
faster vs baseline
Open Quality Risks
6
AI-flagged
Continuous Intelligence Loop
Signal → Decision → Execution → Outcome → Organizational Learning
● Active across all modules
TrendIQ
10 active signals
DecisionIQ
8 initiatives
ActionIQ
7 in execution
EffectivenessIQ
24 outcomes learned
Root-Cause Cycle Time — 12-week trend
Median days to root-cause, by domain
Engineer Time Recaptured
Hours/engineer/week returned by AI assistance, by activity
Trace Coverage Health
Lot → product linkage across the graph
Signal Mix — Last 30 Days
Manufacturing signals by domain
AI Confidence Distribution
Confidence across active recommendations
Top Manufacturing Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — Manufacturing Signal Detection
Signals across MES quality, supplier incoming-inspection, CMMS reliability and process telemetry
● Connected to 7 plant systems
Active Signals
10
↑ 3 this week
Pattern Clusters
5
2 emerging
Cross-Domain Correlations
6
3 high-strength
RCAs in Progress
4
Avg conf 86%
AI Recommendations
5
Pending review
Signals 10
Patterns 5
Correlations 6
Root Cause Analysis 4
Recommendations 5
DecisionIQ — Initiative Management
Active manufacturing initiatives created from accepted signals, with AI-suggested follow-on tasks
8 active initiatives 4 AI suggestions pending
AI Continuously Recommending
The system is learning from 1,914 historical manufacturing outcomes to improve every recommendation below
Initiative owners
94% acceptance
Task owners
89% acceptance
Closure paths
82% accepted
Follow-on tasks
71% accepted
ActionIQ — Execution Coordination
Two-tier Kanban: initiatives at the top, task drill-down on click
Initiatives Kanban
AI Operational Copilot
Live workflow optimizations across the board
Automation Opportunities
Workflows AI can orchestrate end-to-end (with engineer signoff)
EffectivenessIQ — Organizational Learning
What worked, what failed, and how the system is getting better at manufacturing recommendations
● Continuous learning active
Outcomes Captured
1,914
↑ 128 this month
SME Corrections
57
→ model refinement
Initiative Success Rate
86%
↑ from 79% (Q1)
AI Recommendation Accuracy
92%
↑ 6pts vs baseline
Failed Closures Studied
21
Root-caused
Recent Learning Events
Outcomes from closed initiatives feeding back into the recommendation model
Improvement Over Time
Recommendation acceptance rate by quarter
What's Working (Reinforced)
Patterns the AI is doubling down on
What's Not Working (Down-weighted)
Patterns the AI is moving away from
ArchitectureIQ — Enterprise Intelligence Architecture
How fragmented systems, documents and expertise become connected, governed intelligence — from data sources to reasoning agents
◊ 10 source systems · 6 domains

Most AI programs invest in models, GPUs and agents. The highest-performing programs invest in context, relationships and business meaning. ArchitectureIQ is the layered stack that turns enterprise data into that context. Select a layer to inspect it.

Core Domains
All domains become connected through the ontology and graph
AI Agents Across the Enterprise
Specialized agents reasoning over the shared graph
From Search to Reasoning
Enterprise value increases as the platform moves up the stack
Business Value Framework
Typical outcome ranges across the program
OntologyIQ — Enterprise Ontology
The common language of the factory: a consistent understanding of business concepts across every system and location
◊ Steward: Knowledge & Ontology team

The ontology creates the vocabulary; the knowledge graph creates the relationships. Select an entity class to see its properties, source systems and how it connects.

Entity Classes
Relationship Types
Typed connections that give the graph its meaning
A defect is never just a defect — it is associated with a component, supplied by a vendor, manufactured at a plant, and resolved by a corrective action.
SemanticIQ — Semantic Models
Where raw system fields are mapped to governed business concepts — so every system speaks the same language
◊ Semantic model v2.3
System-to-Concept Mappings
Source field → canonical ontology concept, with steward and governance status
Source System / FieldCanonical ConceptDomainStewardStatus
Business Glossary
Shared definitions that anchor the semantic layer
Example Semantic Triples
Subject → predicate → object statements the model asserts
KnowledgeIQ — Living Knowledge Graph
A connected digital representation of the factory — query it in natural language and watch the platform reason across relationships
◊ 48,217 linked artifacts

Engineering Copilot

Ask a question — the copilot reasons across the graph and highlights the entities behind its answer. Days become minutes.
Drag nodes · click to inspect
Add Custom Task
AI has pre-filled this based on the initiative's closure path
AI Pre-fill — Suggestions below are editable. Owner defaults to AI's recommendation.