PP
Mid Stream IQ
Midstream Enterprise Reasoning Platform · Commercial · Engineering · Regulatory · Capital
AI learning from 2,340 midstream outcomes
ENV: PROD · ROLE: BD LEAD
Dashboard
TrendIQ 10
DecisionIQ 8
ActionIQ 8
EffectivenessIQ 24
OntologyIQ 18.4K
KnowledgeIQ 6
GameTheoryIQ 4
Midstream Enterprise Operations
Continuous intelligence across commercial structuring, engineering capacity, regulatory permitting, and capital allocation
◊ Knowledge graph: 18,460 linked entities
Capital Backlog Visibility
94%
↑ 6pts this quarter
Corridor Capacity Coverage
87%
2 corridors monitored
Initiative Closure Rate
82%
8 active initiatives
Commercial Cycle Time
-18%
vs. prior year
Open Enterprise Risks
10
3 critical
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
24 outcomes learned
Commercial Cycle Time — 12-week trend
Term sheet to signed contract, by deal size
Regulatory & Engineering Time Recaptured
Hours saved via pre-filing acceleration and automation
Corridor Capacity Coverage Health
Reserved vs. available headroom by corridor
Signal Mix — Last 30 Days
Share of active signals by domain
AI Confidence Distribution
Across all active signals
Top Enterprise Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — Midstream Enterprise Signal Detection
Signals synthesized across GIS, SCADA, SAP, Contracts, Capital Projects, Regulatory, Finance, and CRM systems
● Connected to 9 enterprise systems
Active Signals
10
↑ 3 this week
Pattern Clusters
4
2 emerging
Cross-Domain Correlations
5
3 high-strength
RCAs in Progress
3
Avg conf 86%
AI Recommendations
4
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 8 AI suggestions pending
AI Continuously Recommending
The system is learning from 2,340 historical midstream 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 BD lead signoff)
EffectivenessIQ — Organizational Learning
What worked, what failed, and how the system is getting better at midstream commercial and engineering recommendations
● Continuous learning active
Outcomes Captured
2,340
↑ 180 this month
SME Corrections
1
→ model refinement
Initiative Success Rate
82%
↑ from 74% (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 enterprise Knowledge Graph
A living semantic model connecting every entity and risk relationship across active midstream enterprise work
◊ 18.4K · 41,200 relationships
Entity Types Modeled
8
Customers, Contracts, Pipeline Assets, Compressor Stations, Capital Projects, Regulatory Filings, Suppliers, Experts
Relationship Types
7
requires · governed-by · supplies · impacts · correlates-with · mitigated-by · historically-precedes
Graph Freshness
96%
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
7
↑ 2 this month
Experts at Retirement/Rotation Risk
6
3 uncaptured
Capture Coverage
58%
Of active work with a linked knowledge asset
Avg. Time-to-Capture
14d
↓ 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
GameTheoryIQ — Financial Scenario Modeling
See exactly how each course of action changes your costs and revenue. Adjust the assumptions and watch the numbers move.
◊ 4 active decision scenarios
Total Enterprise Opportunity — Across All 4 Scenarios
Best option in each scenario, under the assumptions currently set below
Net Year-1 Value
—
Annual Cost Savings
—
Revenue Impact
—
Capital Required
—
Scenario
Adjust the Assumptions
These are real business inputs, not abstract priorities — move them to stress-test the case.
Enterprise Reasoning Chain
How a signal becomes an executive decision — this is the "Game Theory" step, now driven by the numbers above rather than a fixed score
Enterprise Ontologies
→
Knowledge Graph
→
Systems of Understanding
→
Systems of Reasoning
→
Goal Reasoning
→
Game Theory
→
Executive Decision Intelligence
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.