IQ
IntelligenceIQ
Enterprise Intelligence Platform · Ontology-Grounded · Quest Global
AI learning from 2,180 enterprise outcomes
ENV: PROD · ROLE: CHIEF INTELLIGENCE OFFICER
Control Center
TrendIQ 10
DecisionIQ 8
ActionIQ 8
EffectivenessIQ 26
KnowledgeIQ
Enterprise Intelligence Operations
Continuous monitoring, prioritization, and recommended action across every business function — grounded in the enterprise ontology
◇ Knowledge graph: 1.24M linked artifacts · 142 entity classes
Decision Velocity
3.4×
↑ 0.6× QoQ
Knowledge Coverage
92%
+6 pts
Signals Resolved
87%
30-day
Decision Latency
−41%
faster vs baseline
Open Critical Risks
4
AI-prioritized
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
26 outcomes learned
Decision Latency — 12-week trend
Hours from signal to decision, by function
Knowledge Worker Time Recaptured
Hours / week returned to higher-value work
Knowledge Graph Linkage Health
Share of artifacts mapped to the ontology
Signal Mix — Last 30 Days
By business function
AI Confidence Distribution
Across active recommendations
Top Enterprise Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
Enterprise Intelligence Platform
Unified semantic foundation for cross-function data, decisioning, and execution
Platform layer
Knowledge graph grounded Enterprise ontology mapped AI recommendations explainable Closed-loop learning active
TrendIQ — Cross-Function Signal Detection
Digital coworkers monitoring ERP, CRM, PLM, MES, SCM, and document systems in real time
● Connected to 9 enterprise 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
5
Pending review
Signals 12
Patterns 5
Correlations 7
Root Cause Analysis 4
Recommendations 9
DecisionIQ — Initiative Management
Active initiatives created from accepted signals, with AI-suggested follow-on tasks
8 active initiatives 4 AI suggestions pending
AI Continuously Recommending
The system is learning from 2,180 historical enterprise 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 function-lead signoff)
EffectivenessIQ — Organizational Learning
What worked, what failed, and how the system is getting better at enterprise recommendations
● Continuous learning active
Outcomes Captured
2,180
↑ 168 this month
SME Corrections
143
→ model refinement
Initiative Success Rate
84%
↑ from 71% (Q1)
AI Recommendation Accuracy
91%
↑ 9pts vs baseline
Failed Closures Studied
37
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 — The Knowledge Layer
The semantic model, enterprise ontology, and reference architecture that ground every recommendation across the IQ loop
◊ 1.24M linked artifacts · 142 entity classes
Architecture
Ontology Graph 14
Semantic Model 9
A

Four integrated layers. Operational data rises from enterprise systems, is given meaning by the knowledge layer, reasoned over by the AI layer, and converted into business outcomes. Each layer builds on the one below — click a layer to see how it connects to the rest of the platform.

Outcomesvalue rises
Data & documentssignals flow up
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.
KnowledgeIQ — The Knowledge Layer
The semantic model, enterprise ontology, and reference architecture that ground every recommendation across the IQ loop
◊ 1.24M linked artifacts · 142 entity classes
Architecture
Ontology Graph 14
Semantic Model 9
A

Four integrated layers. Operational data rises from enterprise systems, is given meaning by the knowledge layer, reasoned over by the AI layer, and converted into business outcomes. Each layer builds on the one below — click a layer to see how it connects to the rest of the platform.

Outcomesvalue rises
Data & documentssignals flow up