Energy Intelligence Platform
Designing Intelligence: An AI-Augmented Platform for Oil & Gas Efficiency
My role: AI systems designer
Scope: Enterprise database
Duration: September 2024- July 2025
Energy Intelligence Platform
Designing Intelligence: An AI-Augmented Platform for Oil & Gas Efficiency
My role: AI systems designer
Scope: Enterprise database
Duration: September 2024- July 2025
Energy Intelligence Platform
Designing Intelligence: An AI-Augmented Platform for Oil & Gas Efficiency
My role: AI systems designer
Scope: Enterprise database
Duration: September 2024- July 2025
Context
A private oil and gas company in Turkey was drowning in fragmented systems- SCADA screens, spreadsheets, PDFs, and siloed operational data.
The challenge wasn’t just UI. It was cognitive overload.
We needed to build a tool that didn’t just display data, but understood it.
Context
A private oil and gas company in Turkey was drowning in fragmented systems- SCADA screens, spreadsheets, PDFs, and siloed operational data.
The challenge wasn’t just UI. It was cognitive overload.
We needed to build a tool that didn’t just display data, but understood it.
Context
A private oil and gas company in Turkey was drowning in fragmented systems- SCADA screens, spreadsheets, PDFs, and siloed operational data.
The challenge wasn’t just UI. It was cognitive overload.
We needed to build a tool that didn’t just display data, but understood it.
Challenge
Energy analysts, traders, and operations teams rely on fragmented tools that don’t talk to each other. They struggle with:
Lagging data from disparate sources (supply, pricing, emissions, weather)
Manual forecasting and reporting
No clear visibility into energy efficiency or emissions performance
There was a clear opportunity to design an AI-powered system that centralizes intelligence and enhances decision-making.
Challenge
Energy analysts, traders, and operations teams rely on fragmented tools that don’t talk to each other. They struggle with:
Lagging data from disparate sources (supply, pricing, emissions, weather)
Manual forecasting and reporting
No clear visibility into energy efficiency or emissions performance
There was a clear opportunity to design an AI-powered system that centralizes intelligence and enhances decision-making.
Challenge
Energy analysts, traders, and operations teams rely on fragmented tools that don’t talk to each other. They struggle with:
Lagging data from disparate sources (supply, pricing, emissions, weather)
Manual forecasting and reporting
No clear visibility into energy efficiency or emissions performance
There was a clear opportunity to design an AI-powered system that centralizes intelligence and enhances decision-making.
Approach
I started by mapping the entire energy flow lifecycle- from extraction to distribution and identifying inefficiency nodes.
Phase I : Discovery & systems mapping
Operators, engineers, and analysts across each stage of the energy lifecycle each had different insights. By overlaying these qualitative insights with telemetry coverage data, I identified blind spots- from manual bottlenecks in extraction to Excel-based forecasting at the end-use layer.
Phase II : AI integration & interaction design
These inefficiencies weren’t just technical gaps, they were opportunities to reimagine how human expertise could be scaled through intelligent tooling.
We reframed system inefficiencies into prompt templates scoped for LLM reasoning. I designed natural queries like:
“What are the top 3 contributors to pressure loss in Plant C?”
“Show anomalies in pump behavior over time”
Prototyping & decision logic
Built a modular dashboard showing real-time flows and predictive trends
Created “intervention simulation” sliders to test operational decisions
Replaced traditional tables with dynamic flow diagrams and system visuals
Approach
I started by mapping the entire energy flow lifecycle- from extraction to distribution and identifying inefficiency nodes.
Phase I : Discovery & systems mapping
Operators, engineers, and analysts across each stage of the energy lifecycle each had different insights. By overlaying these qualitative insights with telemetry coverage data, I identified blind spots- from manual bottlenecks in extraction to Excel-based forecasting at the end-use layer.
Phase II : AI integration & interaction design
These inefficiencies weren’t just technical gaps, they were opportunities to reimagine how human expertise could be scaled through intelligent tooling.
We reframed system inefficiencies into prompt templates scoped for LLM reasoning. I designed natural queries like:
“What are the top 3 contributors to pressure loss in Plant C?”
“Show anomalies in pump behavior over time”
Prototyping & decision logic
Built a modular dashboard showing real-time flows and predictive trends
Created “intervention simulation” sliders to test operational decisions
Replaced traditional tables with dynamic flow diagrams and system visuals
Approach
I started by mapping the entire energy flow lifecycle- from extraction to distribution and identifying inefficiency nodes.
Phase I : Discovery & systems mapping
Operators, engineers, and analysts across each stage of the energy lifecycle each had different insights. By overlaying these qualitative insights with telemetry coverage data, I identified blind spots- from manual bottlenecks in extraction to Excel-based forecasting at the end-use layer.
Phase II : AI integration & interaction design
These inefficiencies weren’t just technical gaps, they were opportunities to reimagine how human expertise could be scaled through intelligent tooling.
We reframed system inefficiencies into prompt templates scoped for LLM reasoning. I designed natural queries like:
“What are the top 3 contributors to pressure loss in Plant C?”
“Show anomalies in pump behavior over time”
Prototyping & decision logic
Built a modular dashboard showing real-time flows and predictive trends
Created “intervention simulation” sliders to test operational decisions
Replaced traditional tables with dynamic flow diagrams and system visuals
Solution
The platform combines telemetry, LLM insights, and interactive simulations in a single space. It includes:
Live system maps of energy flows and pressure differentials
AI-powered queries for surface-to-causal insight generation
Simulation tools for scenario modeling
Solution
The platform combines telemetry, LLM insights, and interactive simulations in a single space. It includes:
Live system maps of energy flows and pressure differentials
AI-powered queries for surface-to-causal insight generation
Simulation tools for scenario modeling
Solution
The platform combines telemetry, LLM insights, and interactive simulations in a single space. It includes:
Live system maps of energy flows and pressure differentials
AI-powered queries for surface-to-causal insight generation
Simulation tools for scenario modeling
Key features
LLM-Powered Insight Layer
Users ask natural language questions like:
“What caused the spike in Scope 2 emissions last week?” or
“Show me low-cost supply scenarios for Q3 with <20% emissions increase.”
Key features
LLM-Powered Insight Layer
Users ask natural language questions like:
“What caused the spike in Scope 2 emissions last week?” or
“Show me low-cost supply scenarios for Q3 with <20% emissions increase.”
Key features
LLM-Powered Insight Layer
Users ask natural language questions like:
“What caused the spike in Scope 2 emissions last week?” or
“Show me low-cost supply scenarios for Q3 with <20% emissions increase.”
Scenario Planner
Interactive module for comparing cost/carbon tradeoffs.
Built with computational design patterns to handle variable constraints (supply volatility, policy risk).
Scenario Planner
Interactive module for comparing cost/carbon tradeoffs.
Built with computational design patterns to handle variable constraints (supply volatility, policy risk).
Scenario Planner
Interactive module for comparing cost/carbon tradeoffs.
Built with computational design patterns to handle variable constraints (supply volatility, policy risk).
Behind the build
User need -> Model input mapping
I started by collecting real queries from energy analysts. Not just what they asked, but how they asked. These were often vague, multi-layered, and data-dependent ("Why are Q2 carbon levels high?"). We broke them down into structured prompt templates the LLM could understand, while preserving domain-specific nuance
Prompt Engineering + Guardrails
I built a prompt framework that wrapped every question in context: site location, timeframe, and available datasets & scoped responses to known variables. For example:
“Using data from Sites A–C, explain the rise in Scope 2 emissions between May–June.”
LLM + Data Layer Integration
Structured prompts flow through a preprocessing engine, are interpreted by a context-aware LLM, and connected to real-time data via an API layer. The result: scoped, timely answers delivered directly into the analytics stack.
Behind the build
User need -> Model input mapping
I started by collecting real queries from energy analysts. Not just what they asked, but how they asked. These were often vague, multi-layered, and data-dependent ("Why are Q2 carbon levels high?"). We broke them down into structured prompt templates the LLM could understand, while preserving domain-specific nuance
Prompt Engineering + Guardrails
I built a prompt framework that wrapped every question in context: site location, timeframe, and available datasets & scoped responses to known variables. For example:
“Using data from Sites A–C, explain the rise in Scope 2 emissions between May–June.”
LLM + Data Layer Integration
Structured prompts flow through a preprocessing engine, are interpreted by a context-aware LLM, and connected to real-time data via an API layer. The result: scoped, timely answers delivered directly into the analytics stack.
Behind the build
User need -> Model input mapping
I started by collecting real queries from energy analysts. Not just what they asked, but how they asked. These were often vague, multi-layered, and data-dependent ("Why are Q2 carbon levels high?"). We broke them down into structured prompt templates the LLM could understand, while preserving domain-specific nuance
Prompt Engineering + Guardrails
I built a prompt framework that wrapped every question in context: site location, timeframe, and available datasets & scoped responses to known variables. For example:
“Using data from Sites A–C, explain the rise in Scope 2 emissions between May–June.”
LLM + Data Layer Integration
Structured prompts flow through a preprocessing engine, are interpreted by a context-aware LLM, and connected to real-time data via an API layer. The result: scoped, timely answers delivered directly into the analytics stack.
Impact
Reduced insight latency by 40%
Anomaly detection time cut from 12 hours → 15 minutes
Built an interface layer compatible with legacy SCADA systems
Impact
Reduced insight latency by 40%
Anomaly detection time cut from 12 hours → 15 minutes
Built an interface layer compatible with legacy SCADA systems
Impact
Reduced insight latency by 40%
Anomaly detection time cut from 12 hours → 15 minutes
Built an interface layer compatible with legacy SCADA systems
Reflection
This project felt personal. I was translating generational know-how into augmented intelligence, bridging legacy expertise with future systems.
It pushed me to rethink what usable AI really means in high-stakes environments. The goal wasn’t to oversimplify, but to make complexity navigable. I walked away with a deeper conviction: when paired with thoughtful interaction design, LLMs can act as true cognitive partners.
Reflection
This project felt personal. I was translating generational know-how into augmented intelligence, bridging legacy expertise with future systems.
It pushed me to rethink what usable AI really means in high-stakes environments. The goal wasn’t to oversimplify, but to make complexity navigable. I walked away with a deeper conviction: when paired with thoughtful interaction design, LLMs can act as true cognitive partners.
Reflection
This project felt personal. I was translating generational know-how into augmented intelligence, bridging legacy expertise with future systems.
It pushed me to rethink what usable AI really means in high-stakes environments. The goal wasn’t to oversimplify, but to make complexity navigable. I walked away with a deeper conviction: when paired with thoughtful interaction design, LLMs can act as true cognitive partners.
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