SR
SimToRealIQ
Physical AI Engineering Intelligence · CATIA · SIMULIA · DELMIA · ENOVIA · 3DEXPERIENCE
AI learning from 2,140 engineering outcomes
ENV: PROD · ROLE: PROGRAM LEAD
Dashboard
TrendIQ 10
DecisionIQ 8
ActionIQ 8
EffectivenessIQ 22
OntologyIQ 18.4K
KnowledgeIQ 7
ReasoningIQ 214
Physical AI Engineering Operations
Continuous reasoning across design, simulation, software, manufacturing, safety, and field evidence for the RX-7 autonomous mobile robot program
◊ Knowledge graph: 18,460 linked entities
Sim-to-Real Readiness
78%
↑ 6pts this month
Digital Thread Coverage
84%
↑ 3pts vs. last cycle
Revalidation Closure Rate
71%
↑ 9pts this quarter
Analysis Cycle Time
-22%
↓ from 4.1 to 3.2 wks avg
Open Cert/Safety Risks
4
2 critical, 2 high
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
Analysis Cycle Time — 12-week trend
Time from signal detection to engineering recommendation
Engineering Time Recaptured
Hours saved per week, by activity type
Digital Thread Health
Coverage across ENOVIA, DELMIA, SIMULIA and 3DEXPERIENCE
Signal Mix — Last 30 Days
Active signals by engineering domain
AI Confidence Distribution
Recommendation confidence across all active signals
Top Physical AI Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — Physical AI Signal Detection
Signals surfaced across ENOVIA, DELMIA, SIMULIA, 3DEXPERIENCE, and field telemetry systems
● Connected to 5 engineering 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
6
Pending review
Signals 12
Patterns 5
Correlations 7
Root Cause Analysis 4
Recommendations 9
DecisionIQ — Initiative Management
Active Physical AI engineering initiatives created from accepted signals, with AI-suggested follow-on tasks
8 active initiatives 6 AI suggestions pending
AI Continuously Recommending
The system is learning from 2,140 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 engineering lead signoff)
EffectivenessIQ — Organizational Learning
What worked, what failed, and how the system is getting better at Physical AI engineering recommendations
● Continuous learning active
Outcomes Captured
2,140
↑ 186 this month
SME Corrections
34
→ model refinement
Initiative Success Rate
88%
↑ from 79% (Q1)
AI Recommendation Accuracy
91%
↑ 7pts 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
OntologyIQ — Physical AI engineering Knowledge Graph
A living semantic model connecting every entity and risk relationship across active Physical AI engineering work
◊ 18,460 linked entities · 41,200 relationships
Entity Types Modeled
9
Programs, Configurations, Requirements, Simulation Models, Sensors/Controllers, Manufacturing Processes, Safety Objectives, Suppliers, Field Events
Relationship Types
14
requires · governed-by · verified-by · substituted-into · documented-in · correlates-with…
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 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
9
↑ 3 this month
Experts at Retirement/Rotation Risk
7
4 uncaptured
Capture Coverage
71%
Of active work with a linked knowledge asset
Avg. Time-to-Capture
13d
↓ from 21d 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 Physical AI Recommendations
Executives need recommendations, not just answers — the reasoning engine evaluates multiple Physical AI factors together to produce a single evidence-backed recommendation
◊ Reasoning over the OntologyIQ knowledge graph
Reasoning Chains Run
214
↑ this week
Avg. Factors Evaluated
5.2
per recommendation
Avg. Recommendation Confidence
85%
Grounded in graph evidence
Executive Recommendations Delivered
11
This month
From Systems of Record to Systems That Reason
No existing system is replaced — each becomes more valuable feeding the reasoning engine
CATIA · SIMULIA · DELMIA · ENOVIA · 3DEXPERIENCE
5 systems connected
→
Enterprise Ontology
9 entity types
→
Knowledge Graph
18,460 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.