Continuous engineering memory across programs — turning fragmented records into reusable knowledge
◊ Knowledge graph: 47,392 linked artifacts
First-Time-Right Rate
91.4%
↑ 3.2 pts vs LY
Engineering Memory Coverage
88%
47,392 artifacts linked
Recurrence Prevention
73%
issues caught pre-build
Investigation Cycle Time
−60%
faster root cause
Open Recurrence Risks
5
3 high-severity
Continuous Intelligence Loop
Signal → Decision → Execution → Outcome → Organizational Learning
● Active across all modules
TrendIQ
9 active signals
→
DecisionIQ
8 initiatives
→
ActionIQ
7 in execution
→
EffectivenessIQ
24 outcomes learned
Investigation Cycle Time — 12-week trend
Mean time from defect signal to root-cause closure
Engineer Time Recaptured
Hours/week returned by reuse vs re-investigation
Engineering Memory Linkage Health
Artifact linkage coverage by domain
Signal Mix — Last 30 Days
Detected engineering signals by category
AI Confidence Distribution
Confidence band across active recommendations
Top Engineering Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — Engineering Signal Detection
Signals surfaced from PLM, QMS, MES, and test records across active programs
● Connected to 6 engineering systems
Active Signals
9
↑ 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 9
Patterns 5
Correlations 6
Root Cause Analysis 4
Recommendations 5
DecisionIQ — Initiative Management
Active engineering initiatives created from accepted signals, with AI-suggested follow-on tasks
8 active initiatives5 AI suggestions pending
AI Continuously Recommending
The system is learning from 1,847 historical engineering 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 engineering recommendations
● Continuous learning active
Outcomes Captured
1,847
↑ 132 this month
SME Corrections
96
→ model refinement
Initiative Success Rate
87%
↑ from 71% (Q1)
AI Recommendation Accuracy
91%
↑ 9 pts vs baseline
Failed Closures Studied
14
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
KnowledgeIQ — Engineering Memory
The living knowledge layer beneath the loop: ontology, semantic models, and a knowledge graph that connect Requirements, Designs, Parts, Failure Modes, Root Causes, Corrective Actions, Suppliers, Test Records and Programs into reusable memory.
◊ 47,392 linked artifacts
Linked Artifacts
47,392
↑ 2,140 this quarter
Ontology Classes
142
↑ 9 refined
Entity Resolution
96.4%
↑ 1.8 pts
Knowledge Reuse Events
1,284
↑ 318 this quarter
Avg Investigation Time Saved
60%
via reuse-first matching
Modern manufacturers don't lack data — they lack the mechanism to recall it when needed. KnowledgeIQ is that mechanism. It transforms fragmented operational records into a structured, semantically linked memory so that every signal in TrendIQ can be matched against what the enterprise has already solved — turning organizational forgetting into organizational learning at scale.
6,940
reusable solutions in memory
Knowledge Graph 30
Ontology 142
Semantic Models 8
Knowledge Reuse 5
Questions 9
Drag nodes · click to inspect · scroll to zoom
Domain Ontology — class hierarchy
From Systems of Record to a Semantic Layer
How fragmented source records resolve into canonical, reusable knowledge entities
7
Source systems
→
2.1M
Raw records
→
142
Ontology classes
→
47,392
Linked entities
→
96.4%
Resolved
Semantic Mapping Catalog
Source entity → canonical concept, with the resolution rule and match confidence
● 8 active mappings
Proven Solutions Surfaced From Memory
When a new problem matches the graph, AI surfaces the prior solution instead of restarting the investigation
Engineering Memory Growth
Linked artifacts — strengthens with every project
Why this matters
The fastest way to reduce engineering cost is to stop solving the same problem twice. Every closed initiative in EffectivenessIQ writes back into this memory, so the next match is faster and more confident than the last.
Questions FactoryIQ Can Answer From the Graph
Each question is resolved by traversing the ontology — click to run it against the live knowledge graph
◊ ask the graph
These aren't dashboard lookups — they're questions that require connecting evidence across systems (PLM, QMS, MES, SCADA, SRM, LIMS, ERP). The ontology defines the relationships; the knowledge graph walks them. Click any question to jump to the graph and inspect the entities and provenance that answer it.
AReuse & recall — "has this been solved before?"
Has this thermal-fatigue cracking been solved before — and what's the validated fix?