MQ
MidstreamIQ
Midstream Enterprise Intelligence Platform · SCADA · GIS · EAM · Commercial Systems
AI learning from 2,140 operational outcomes
ENV: PROD · ROLE: OPS EXEC
Dashboard
TrendIQ 9
DecisionIQ 8
ActionIQ 8
EffectivenessIQ 26
OntologyIQ 18,460 linked entities
KnowledgeIQ 6
ReasoningIQ 214
Midstream Operations Intelligence
Continuous intelligence across the compressor fleet, pipeline capacity, integrity, and commercial commitments
◊ Knowledge graph: 18,460 linked entities
Corridor Service Reliability
97%
↑ 1.2pts vs last quarter
Asset Integrity Coverage
94%
2 restrictions active
Maintenance Closure Rate
88%
On-time closure this quarter
Unplanned Outage Cycle Time
-18%
↓ vs prior year
Open Capacity/Integrity Risks
7
↓ 2 this month
Continuous Intelligence Loop
Signal → Decision → Execution → Outcome → Organizational Learning
● Active across all modules
TrendIQ
9 active signals
→
DecisionIQ
8 initiatives
→
ActionIQ
7 in execution
→
EffectivenessIQ
26 outcomes learned
Corridor Capacity Utilization — 12-week trend
Corridor 3 vs. network average
Field Crew Time Recaptured
Hours saved per week, by activity
Asset Integrity Coverage Health
Inspection and restriction coverage by segment
Signal Mix — Last 30 Days
By operating domain
AI Confidence Distribution
Across all active recommendations
Top Midstream Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — Midstream Signal Detection
Signals detected across SCADA, EAM, GIS, Supply Chain, and Commercial systems
● Connected to 6 midstream systems
Active Signals
9
↑ 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 midstream 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 2,140 historical operational 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 ops lead signoff)
EffectivenessIQ — Organizational Learning
What worked, what failed, and how the system is getting better at midstream recommendations
● Continuous learning active
Outcomes Captured
2,140
↑ 64 this month
SME Corrections
14
→ model refinement
Initiative Success Rate
84%
↑ from 76% (Q1)
AI Recommendation Accuracy
89%
↑ 6pts vs baseline
Failed Closures Studied
5
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 — midstream Knowledge Graph
A living semantic model connecting every entity and risk relationship across active midstream work
◊ 18,460 linked entities · 41,200 relationships
Entity Types Modeled
9
Assets, Segments, Capacity, Contracts, Customers, Maintenance, Parts, Sites, Routes
Relationship Types
12
impacts · constrains · serves · depends on · supplies · feeds · mitigated-by…
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 pipeline operator & 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
31
↑ 5 this month
Experts at Retirement/Rotation Risk
6
2 uncaptured
Capture Coverage
68%
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 midstream enterprise Recommendations
Executives need recommendations, not just answers — the reasoning engine evaluates multiple midstream enterprise factors together to produce a single evidence-backed recommendation
◊ Reasoning over the OntologyIQ knowledge graph
Reasoning Chains Run
214
↑ this week
Avg. Factors Evaluated
5.4
per recommendation
Avg. Recommendation Confidence
88%
Grounded in graph evidence
Executive Recommendations Delivered
12
This month
From Systems of Record to Systems That Reason
No existing system is replaced — each becomes more valuable feeding the reasoning engine
SCADA · GIS · EAM · Supply Chain · Commercial Systems
6 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
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