EQ
EngineeringIQ
Engineering Reasoning Platform · Ontologies · Knowledge Graphs · Semantic Models
AI learning from 3,412 engineering outcomes
ENV: PROD · ROLE: CHIEF ENGINEER
Dashboard
TrendIQ 11
DecisionIQ 8
ActionIQ 8
EffectivenessIQ 22
OntologyIQ 9
KnowledgeIQ 6
ReasoningIQ 4
SemanticIQ 10
Engineering Reasoning Operations
Continuous reasoning across products, functions, software, electronics, and field performance — powered by ontologies, knowledge graphs, and semantic models
◊ Knowledge graph: 68,214 linked engineering artifacts
Engineering Reuse Rate
41%
↑ 6pts this quarter
Ontology Link Coverage
87%
↑ 4pts this quarter
Reasoning Query Resolution
94%
↑ 2pts this month
Validation Cycle Time
-22%
vs. pre-reasoning baseline
Open Engineering Risks
5
3 critical
Continuous Intelligence Loop
Signal → Decision → Execution → Outcome → Organizational Learning
● Active across all modules
TrendIQ
11 active signals
→
DecisionIQ
8 initiatives
→
ActionIQ
7 in execution
→
EffectivenessIQ
22 outcomes learned
Validation Cycle Time — 12-week trend
Hours per validation cycle, by engineering domain
Engineering Time Recaptured
Hours per engineer per week, before vs. after reasoning
Knowledge Graph Linkage Health
Share of engineering artifacts with current, stale, or missing semantic links
Signal Mix — Last 30 Days
Reasoning signals by engineering domain
AI Confidence Distribution
Reasoning engine 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 Reasoning Signal Detection
Signals reasoned across PLM, ALM, CAD, requirements, GitHub, test, manufacturing, service, and warranty systems via the knowledge graph
● Connected to 9 engineering systems
Active Signals
11
↑ 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 engineering initiatives created from accepted reasoning signals, with AI-suggested follow-on tasks
8 active initiatives 3 AI suggestions pending
AI Continuously Recommending
The system is learning from 3,412 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 reasoning-driven optimizations across the board
Automation Opportunities
Workflows the reasoning engine can orchestrate end-to-end (with engineering lead signoff)
EffectivenessIQ — Organizational Learning
What worked, what failed, and how the reasoning engine is getting better at engineering recommendations
● Continuous learning active
Outcomes Captured
3,412
↑ 214 this month
SME Corrections
18
→ model refinement
Initiative Success Rate
86%
↑ from 74% (Q1)
AI Recommendation Accuracy
89%
↑ 11pts vs baseline
Failed Closures Studied
14
Root-caused
Recent Learning Events
Outcomes from closed initiatives feeding back into the reasoning 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 — engineering Knowledge Graph
A living semantic model connecting every entity and risk relationship across active engineering work
◊ 68,214 linked entities · 142,600 relationships
Entity Types Modeled
9
Products, Functions, Software, Electronics, Requirements, Validation, Manufacturing, Suppliers, Diagnostics
Relationship Types
14
controls · communicates-via · constrained-by · verified-by · sourced-from…
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
7
↑ 2 this month
Experts at Retirement/Rotation Risk
6
3 uncaptured
Capture Coverage
62%
Of active work with a linked knowledge asset
Avg. Time-to-Capture
14d
↓ from 26d 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
SemanticIQ — Semantic Model & Standards Mapping
The taxonomy, relationship types, and terminology model underneath the knowledge graph, plus semantic matching against historical cases
◊ 7 domains · 9 categories · 12 failure modes
Concepts Modeled
21
9 categories + 12 failure modes across 7 domains
Standards Coverage
78%
ISO 26262 / ASPICE / IATF 16949 / internal standards mapped
Semantic Match Precision
89%
Validated against SME review
Taxonomy Depth
3
Domain → Category → Failure Mode
Terms Normalized
6
Canonical concepts unifying source-system aliases
Risk Taxonomy
Click a domain to expand its categories, then a category to see the specific failure modes AI is watching for
Semantic Relationship Types
The edge vocabulary the model uses to connect concepts, signals, and standards
Canonical Term Mapping
Normalizes the different names each source system uses for the same concept
Find Semantically Similar Cases
Search by concept, not keyword — matches on meaning across the historical corpus
Standards Mapping
Internal risk domains mapped to the governing regulatory clause, with current evidence coverage
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
312
↑ this week
Avg. Factors Evaluated
4.2
per recommendation
Avg. Recommendation Confidence
86%
Grounded in graph evidence
Executive Recommendations Delivered
14
This month
From Systems of Record to Systems That Reason
No existing system is replaced — each becomes more valuable feeding the reasoning engine
PLM · ALM · CAD · GitHub · Test Systems
9 systems connected
→
Enterprise Ontology
9 entity types
→
Knowledge Graph
68,214 linked entities
→
Reasoning Engine
312 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.