CE
ConnectedEngineeringIQ
Orthopedic Engineering Intelligence · PLM · MES · QMS · Inspection · Documents
AI learning from 1,486 engineering outcomes
ENV: ILLUSTRATIVE DATA · ROLE: ENGINEERING LEAD
Dashboard
TrendIQ 10
DecisionIQ 9
ActionIQ 9
EffectivenessIQ 24
OntologyIQ 9
KnowledgeIQ 6
ReasoningIQ 4
Connected Engineering Operations
Fictional example for discussion: how ontologies and knowledge graphs support change assessment, defect investigation, manufacturing readiness and knowledge reuse
◊ Knowledge graph: 12,480 linked records
Change Review Completeness
91%
↑ 6pts vs. manual baseline
Record Linkage Coverage
87%
↑ 4pts this quarter
Readiness Evidence Coverage
78%
↑ 9pts this quarter
Investigation Cycle Time
-31%
↓ vs. prior 2 quarters
Open Readiness Gaps
7
2 revision conflicts
Continuous Intelligence Loop
Signal → Decision → Execution → Outcome → Organizational Learning
● Active across all modules
TrendIQ
10 active signals
→
DecisionIQ
9 initiatives
→
ActionIQ
8 in execution
→
EffectivenessIQ
24 outcomes learned
Assessment Effort — 12-week trend
Hours to assemble connected evidence per review
Engineer Time Recaptured
Hours per engineer per week spent assembling information
Record Linkage Health
Share of engineering records linked at the current revision
Signal Mix — Last 30 Days
Open signals by engineering domain
AI Confidence Distribution
Confidence across active signals
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 assembled from PLM, manufacturing, quality, inspection and document sources
● Connected to 5 engineering systems
Active Signals
10
↑ 3 this week
Pattern Clusters
5
2 emerging
Cross-Domain Correlations
7
3 high-strength
RCAs in Progress
3
Avg conf 86%
AI Recommendations
5
Pending review
Signals 12
Patterns 5
Correlations 7
Root Cause Analysis 4
Recommendations 9
DecisionIQ — Initiative Management
Active engineering initiatives created from accepted signals, with AI-suggested follow-on tasks
9 active initiatives 8 AI suggestions pending
AI Continuously Recommending
The system is learning from 1,486 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
1,486
↑ 74 this month
SME Corrections
112
→ model refinement
Initiative Success Rate
84%
↑ from 71% (Q1)
AI Recommendation Accuracy
88%
↑ 11pts vs baseline
Failed Closures Studied
17
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
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 — Engineering Knowledge Graph
A living semantic model connecting every entity and risk relationship across active Engineering work
◊ 12,480 linked records · 31,900 relationships
Entity Types Modeled
9
Variant, Component, Characteristic, Operation, Tool, Inspection, Evidence, Change, Nonconformance
Relationship Types
12
manufactured by · uses · checks · supports · affects…
Graph Freshness
94%
Entities updated within the last cycle
Query Accuracy (validated)
91%
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
24
↑ 4 this month
Experts at Retirement/Rotation Risk
6
2 not started
Capture Coverage
64%
Of active work with a linked knowledge asset
Avg. Time-to-Capture
12d
↓ from 18d 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 Engineering Recommendations
Executives need recommendations, not just answers — the reasoning engine evaluates multiple Engineering factors together to produce a single evidence-backed recommendation
◊ Reasoning over the OntologyIQ knowledge graph
Reasoning Chains Run
186
↑ this week
Avg. Factors Evaluated
5.2
per recommendation
Avg. Recommendation Confidence
86%
Grounded in graph evidence
Executive Recommendations Delivered
9
This month
From Systems of Record to Systems That Reason
No existing system is replaced — each becomes more valuable feeding the reasoning engine
Existing Engineering Systems
PLM · MES · QMS · Inspection · Documents
→
Enterprise Ontology
9 entity types
→
Knowledge Graph
12,480 linked records
→
Reasoning Engine
186 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