FI
FabIQ
Enterprise Reasoning Platform · Equipment Health + Fab Operations
AI learning from 2,340 equipment & fab outcomes
ENV: PROD · ROLE: FIELD ENGINEERING LEAD
Dashboard
TrendIQ 10
DecisionIQ 8
ActionIQ 8
EffectivenessIQ 22
OntologyIQ 9
KnowledgeIQ 6
ReasoningIQ 4
Fleet & Fab Enterprise Operations
Continuous reasoning across EUV source health, customer production, and supply chain
◊ Knowledge graph: 18,240 linked entities
Fleet Availability
96.4%
↑ 0.8pts vs last quarter
Source Health Coverage
91%
↑ 3pts
Signal-to-Initiative Closure
82%
↑ 6pts
Unplanned Downtime
-18%
↑ improvement vs baseline
Open Fleet Risks
7
2 critical
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
22 outcomes learned
Fleet Availability — 12-week trend
High-NA and legacy EUV systems, fleet-wide
Field Engineering Time Recaptured
Hours saved per week via predictive scheduling
Source Health Coverage
Telemetry-covered subsystems across the fleet
Signal Mix — Last 30 Days
By domain: source, optics, supply chain, scheduling
AI Confidence Distribution
Across active signals and recommendations
Top Fleet & Fab Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — Fleet & Fab Signal Detection
Signals from EUV telemetry, service records, customer MES/ERP feeds, and spare parts systems
● Connected to 6 fleet & fab 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 12
Patterns 5
Correlations 7
Root Cause Analysis 4
Recommendations 9
DecisionIQ — Initiative Management
Active fleet & fab initiatives created from accepted signals, with AI-suggested follow-on tasks
8 active initiatives 5 AI suggestions pending
AI Continuously Recommending
The system is learning from 2,340 historical equipment & fab 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 field engineering lead signoff)
EffectivenessIQ — Organizational Learning
What worked, what failed, and how the system is getting better at fleet & fab recommendations
● Continuous learning active
Outcomes Captured
2,340
↑ 180 this month
SME Corrections
34
→ model refinement
Initiative Success Rate
79%
↑ from 68% (Q1)
AI Recommendation Accuracy
90%
↑ 7pts vs baseline
Failed Closures Studied
11
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
OntologyIQ — fleet & fab Knowledge Graph
A living semantic model connecting every entity and risk relationship across active fleet & fab work
◊ 18,240 linked entities · 41,600 relationships
Entity Types Modeled
9
Systems, Subsystems, Components, Signals, Initiatives, Customer Sites, Production Lines, Maintenance Windows, Spare Parts
Relationship Types
12
has-subsystem · exhibits · triggers · correlates-with · root-caused-by · scheduled-during · owned-by
Graph Freshness
96%
Entities updated within the last cycle
Query Accuracy (validated)
93%
Against SME-reviewed answer set
Ask the Knowledge Graph
Not sure where to start? Try a sample question below, or type your own — answers are traced back to the underlying graph nodes and edges.
◊ Grounded answers only
Knowledge Graph Schema
Core entity and relationship types behind every answer
Highest-Connectivity Entities
Nodes with the most cross-domain relationships — usually the highest-leverage risk points
KnowledgeIQ — Institutional & Tacit Knowledge Capture
Tracks where field engineering know-how is captured, at risk, or missing entirely, and visualizes it as a knowledge graph
◊ Linked to the OntologyIQ knowledge graph
Knowledge Assets Captured
31
↑ 6 this month
Experts at Retirement/Rotation Risk
6
3 not started
Capture Coverage
68%
Of active work with a linked knowledge asset
Avg. Time-to-Capture
11d
↓ from 19d last quarter
Knowledge Graph — Experts, Assets, Programs & Sites
Instance-level view of the graph: who holds the knowledge, what's been captured, and which programs and sites it feeds. Click any node.
At-Risk Expertise
Operators and specialists nearing retirement or rotation, ranked by capture status
Knowledge Asset Library
Captured video, process sheets, and annotated drawings, linked to the program and initiative they came from
ReasoningIQ — Evidence-Backed enterprise Recommendations
Executives need recommendations, not just answers — the reasoning engine evaluates multiple enterprise factors together to produce a single evidence-backed recommendation
◊ Reasoning over the OntologyIQ knowledge graph
Reasoning Chains Run
214
↑ this week
Avg. Factors Evaluated
4.8
per recommendation
Avg. Recommendation Confidence
88%
Grounded in graph evidence
Executive Recommendations Delivered
12
This month
From Systems of Record to Systems That Reason
No existing system is replaced — each becomes more valuable feeding the reasoning engine
Equipment Telemetry · Service Records · Customer MES/ERP · Spares System
6 systems connected
→
Enterprise Ontology
9 entity types
→
Knowledge Graph
18,240 linked entities
→
Reasoning Engine
214 chains run
→
Business Users
Executive decisions
Reasoning Scenarios
Each scenario evaluates multiple factors across the enterprise before producing a recommendation — click a scenario to see the full evidence chain
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.
OntologyIQ — fleet & fab Knowledge Graph
A living semantic model connecting every entity and risk relationship across active fleet & fab work
◊ 18,240 linked entities · 41,600 relationships
Entity Types Modeled
9
Systems, Subsystems, Components, Signals, Initiatives, Customer Sites, Production Lines, Maintenance Windows, Spare Parts
Relationship Types
12
has-subsystem · exhibits · triggers · correlates-with · root-caused-by · scheduled-during · owned-by
Graph Freshness
96%
Entities updated within the last cycle
Query Accuracy (validated)
93%
Against SME-reviewed answer set
Ask the Knowledge Graph
Not sure where to start? Try a sample question below, or type your own — answers are traced back to the underlying graph nodes and edges.
◊ Grounded answers only
Knowledge Graph Schema
Core entity and relationship types behind every answer
Highest-Connectivity Entities
Nodes with the most cross-domain relationships — usually the highest-leverage risk points
KnowledgeIQ — Institutional & Tacit Knowledge Capture
Tracks where field engineering know-how is captured, at risk, or missing entirely, and visualizes it as a knowledge graph
◊ Linked to the OntologyIQ knowledge graph
Knowledge Assets Captured
31
↑ 6 this month
Experts at Retirement/Rotation Risk
6
3 not started
Capture Coverage
68%
Of active work with a linked knowledge asset
Avg. Time-to-Capture
11d
↓ from 19d last quarter
Knowledge Graph — Experts, Assets, Programs & Sites
Instance-level view of the graph: who holds the knowledge, what's been captured, and which programs and sites it feeds. Click any node.
At-Risk Expertise
Operators and specialists nearing retirement or rotation, ranked by capture status
Knowledge Asset Library
Captured video, process sheets, and annotated drawings, linked to the program and initiative they came from
ReasoningIQ — Evidence-Backed enterprise Recommendations
Executives need recommendations, not just answers — the reasoning engine evaluates multiple enterprise factors together to produce a single evidence-backed recommendation
◊ Reasoning over the OntologyIQ knowledge graph
Reasoning Chains Run
214
↑ this week
Avg. Factors Evaluated
4.8
per recommendation
Avg. Recommendation Confidence
88%
Grounded in graph evidence
Executive Recommendations Delivered
12
This month
From Systems of Record to Systems That Reason
No existing system is replaced — each becomes more valuable feeding the reasoning engine
Equipment Telemetry · Service Records · Customer MES/ERP · Spares System
6 systems connected
→
Enterprise Ontology
9 entity types
→
Knowledge Graph
18,240 linked entities
→
Reasoning Engine
214 chains run
→
Business Users
Executive decisions
Reasoning Scenarios
Each scenario evaluates multiple factors across the enterprise before producing a recommendation — click a scenario to see the full evidence chain