AE
AdaptiveEngineeringIQ
Engine Controls Intelligence Platform · DO-178C / DO-254 / ARP4754A
AI learning from 1,847 engineering outcomes
ENV: AIR-GAPPED · ROLE: V&V LEAD
Dashboard
TrendIQ 12
DecisionIQ 8
ActionIQ 8
EffectivenessIQ 24
Adaptive Engineering Operations
Continuous certification readiness across the engine controls program lifecycle
◊ Knowledge graph: 47,392 linked artifacts
Certification Readiness
87%
↑ 14% vs prior cycle
Trace Coverage
94.2%
↑ 6.1% wk/wk
MC/DC Closure
91.6%
↑ 3.4% wk/wk
V&V Cycle Time
-22%
vs Q1 baseline
Open Cert Risks
7
↑ 2 new this week
Continuous Intelligence Loop
Signal → Decision → Execution → Outcome → Organizational Learning
● Active across all modules
TrendIQ
12 active signals
→
DecisionIQ
8 initiatives
→
ActionIQ
8 in execution
→
EffectivenessIQ
24 outcomes learned
Verification Cycle Time — 12-week trend
Hours per MC/DC closure cycle across FADEC, fuel metering, thrust governor modules
Engineering Time Recaptured
Hours/week per engineer, by activity
Trace Link Health
Requirement ↔ Code ↔ Test linkage status
Signal Mix — Last 30 Days
Where AI is detecting engineering risk
AI Confidence Distribution
Across active recommendations
Top Cert Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — Engineering Signal Detection
Continuous detection of certification gaps, coverage anomalies, and traceability drift across DOORS, Cameo, Git, LDRA, TestStand, and PLM repositories
● Connected to 8 engineering systems
Active Signals
12
↑ 3 this week
Pattern Clusters
5
2 emerging
Cross-Domain Correlations
7
3 high-strength
RCAs in Progress
4
Avg conf 86%
AI Recommendations
9
Pending review
Signals 12
Patterns 5
Correlations 7
Root Cause Analysis 4
Recommendations 9
DecisionIQ — Initiative Management
Active certification-readiness initiatives created from accepted signals, with AI-suggested follow-on tasks
8 active initiatives 12 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
↑ 124 this month
SME Corrections
238
→ model refinement
Initiative Success Rate
89%
↑ from 71% (Q1)
AI Recommendation Accuracy
91%
↑ 8pts vs baseline
Failed Closures Studied
31
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.