From the ground to the retail. Lumina is the reasoning layer that turns your operational data into intelligence using AI. Built by industry experts, backed by 25+ years of experience.
No sign-up required. Your data stays in your environment. Local-first architecture.
The Strategic Gap
The AI market in oil and gas is projected to grow from $4 billion in 2025 to $7.5 billion by 2030 (ResearchAndMarkets, 2026). McKinsey reports that integrated AI systems can reduce operating expenditures by up to 20% and increase production efficiency by 5 to 8%. Yet the majority of companies remain stuck in pilot phases. The obstacle isn't data or technology. It's the gap between data and action, caused by delays in reasoning, fragmented tools, and AI that can't explain itself.
Your data scientists build models for specific problems. Each takes months. It serves one team. When the builder leaves, the knowledge leaves with them. You can't scale this across an organization with hundreds of assets and dozens of operating units.
Billions invested in data lakes and BI tools. The dashboards are built. The reports run. And your operators still don't know what to do tomorrow morning. Display is not intelligence. You need a system that reasons, not one that reflects.
Experienced operators are retiring with decades of pattern recognition that no dashboard captures. The next generation has the tools but not the judgment. That judgment needs to be codified in a system, not lost to attrition.
General-purpose AI tools get production averages wrong, invent well names, and can't explain their reasoning. When burst pressure calculations or regulatory compliance are at stake, 'approximately right' is not acceptable. You need computation you can verify.
ABSA, CEPA, and PHMSA require data to be auditable, traceable, and verifiable. Sending operational data, safety records, and integrity assessments to third-party cloud infrastructure introduces compliance risk your legal team won't accept. And shouldn't.
Separate analytics tools, custom pipelines, and siloed ML models across production, integrity, safety, and commercial. Each maintained independently. Each with its own technical debt. One reasoning layer that works across all of them changes the economics entirely.
What Lumina Does
Lumina doesn't replace your engineers or your existing systems. It adds a reasoning layer on top, turning the data you already collect into intelligence your teams can act on, verify, and trust.
Lumina's Exploration engine continuously runs hypothesis tests: anomaly clustering, correlation analysis, severity scoring, Benford's Law. It delivers findings proactively, the way an experienced operator spots flags, something that 'doesn't look right'.
Production, maintenance, lab, safety, weather, pricing. Through the Dialogue Intelligence Framework™, a six-layer cognitive architecture, agents reason across all of them simultaneously. Fragmented data becomes connected understanding.
With Lumina ML, knowledge stays with the developer. Build Lumina it once with the agent. Configured by your engineers, trained on your procedures, compounding context over time. Each interaction builds a knowledge base that scales.
Production vs safety vs compliance. Tensions that take days in meetings are resolved in seconds. Agents vote, alert, challenge each other with every position grounded in data and rationale visible.
Every query is valid SQL. Every calculation executed by a database, not an LLM. When your integrity engineer says 'How did you calculate that?', the agent shows the query, the data, and the method.
Your data doesn't move to us. SaaS, private cloud, on-premises, or air-gapped. Bring your own LLM through Ollama, or export agents as a chill files for Claude, Copilot, or any MCP-compatible tool.
How It Works
Most AI tools are wrappers around a single language model. Lumina is built on DIF, a six-layer cognitive architecture that treats dialogue as the primary signal for insight, learning, and decision-making. Each layer serves a distinct purpose.
Ask questions in plain language. Agents respond with reasoned answers, suggest follow-up questions, and guide you toward the decision you need to make.
Proactive scan engine for your data: cluster analysis, correlation, Benford's law, severity scoring. Proactive hypothesis testing that surfaces what you didn't know to look for.
Raw findings are synthesized into composites that connect cause, context, and consequence. Not just 'this is anomalous' but 'here is what it means for your operation'.
A six-engine pipeline (DSP, Policy, Semantic Guard, LLM, Classifier, Memory, Audit) judges what agents can access, say, and do. Per-agent configuration. Zero trust by default.
Agents build context over time through a three-tier memory architecture: Local knowledge, team-level awareness, and organizational intelligence that compounds with use.
Reasoning and tools are separated by design. LLMs plan the query. DuckDB executes the calculation on your data. No black-box results. No hallucination loops. Every step visible.
This is not a chatbot with a database connection. It is a complete cognitive architecture where every layer serves a purpose and every output is traceable.
Explore the Full DIF ArchitectureUse Cases by Domain
SAGD, Conventional, Mining Operations, Exploration
Upstream operations generate some of the most data-rich environments in the industry: wellhead sensors, downhole gauges, SCADA historians, production accounting systems, and regulatory filings. McKinsey reports that upstream companies using advanced analytics and AI have captured additional value of more than $5 per barrel of oil equivalent. Predictive maintenance alone can lower costs by up to 25% while improving asset availability (McKinsey Global Institute). The opportunity isn't in collecting more data. It's in closing the gap between what you measure and what you act on.
Well optimization, Arps decline curve analysis, water cut surveillance, GOR tracking, ESP health diagnostics, artificial lift optimization, production forecasting (DFIT), deferred production accounting, well test validation, and reserves updating.
Real-time WITSML data analysis, Mechanical Specific Energy (MSE) optimization, torque & drag modeling, kick detection and influx monitoring, flat time reduction, Non-Productive Time (NPT) analysis, bit grade analysis, bit-on-bottom drilling optimization, mud weight and ECD monitoring, well trajectory optimization.
TRIR tracking and trend analysis, near-miss pattern recognition, incident root cause correlation, fatigue-risk and shift-pattern analytics, permit-to-work intelligence, management OFI (dashboard), asset chemistry and disposal, reclamation progress tracking, wildlife monitoring near assets, GHG quantification under CCIR, fugitive emission reporting, PHMSA TVC documentation.
Fugitive emissions (LDAR) analytics, flare and vent monitoring, tailings management (OFI dashboard), water chemistry and disposal, reclamation progress tracking, wildlife monitoring near assets, GHG quantification under CCIR, Plus operators with power generation can combine production data with real-time grid pricing via Lumina to optimize where to buy or sell power.
Upstream using all oil and gas production optimisation have recorded a 3-10% throughput increase if reported by McKinsey.
NPT costs the industry an estimated $25–$50 billion annually. A drilling intelligence platform could reduce NPT by 10–30%. (2018-2023)
Pipelines, Compressor Stations, LNG Plants, Terminals, Gathering Systems
Midstream is where asset integrity, safety, and throughput optimization converge under the most intense regulatory scrutiny. PHMSA's pipeline safety regulations (49 CFR Parts 192, 195) and CEPA's OPR-99 demand traceable, verifiable records for every integrity decision. This is also where the most expensive single-point failures occur, and where AI reasoning has the highest ROI when it catches problems early. McKinsey reports that condition-based maintenance in pipeline and compression operations has reduced costs by up to 27% while increasing reliability and uptime.
In-Line Inspection (ILI) data analysis, CEPA/PHMSA compliance, anomaly clustering and interaction rules, burst pressure calculations (B31G, Modified B31G, RSTRENG), defect classification (probability of exceedance for remaining life assessment), dig program optimization with cost-benefit analysis, soil-coating interaction analysis, cathodic protection monitoring, field note correlation with structured data.
Compressor station performance monitoring, flow assurance optimization, linepack management, scheduling and nomination analysis, leak detection correlation, pigging program intelligence, gas quality and hydrocarbon dew point monitoring, custody transfer accuracy.
Predictive maintenance in midstream operations has driven up to 27% reduction in maintenance costs while increasing asset uptime by up to 15% (McKinsey).
Refineries, Petrochemical, Retail, Marketing, Distribution
Downstream operations is where crude becomes product and product meets the market. Refineries operate complex, heat-integrated processes where small changes in feed quality or operating conditions cascade through every unit. Retail and distribution add supply chain complexity: inventory management, pricing optimization, and customer demand patterns. McKinsey reports that end-to-end AI optimization in downstream operations has delivered cost savings exceeding $1 per barrel, while digitally enabled B2C operations have raised EBITDA by 5 to 10% through network planning, assortment optimization, and pricing intelligence.
Crude blend optimization and assay analysis, APC cut-point management, product yield maximization, reformer severity optimization, furnace efficiency monitoring, heat integration analysis, turnaround planning intelligence, catalyst performance tracking, product quality prediction and SPC, energy and steam balance optimization.
Spare parts inventory optimization, vendor performance analysis, maintenance scheduling intelligence (turnarounds, shutdowns), procurement pattern analysis, warehouse stock-level reasoning, logistics route optimization, contractor management and cost tracking.
Fuel station performance analysis (monitoring weather, traffic, pricing, product placement), demand forecasting by location, pricing optimization across product grades, distribution network efficiency, customer behavior patterns, promotional effectiveness, competitor pricing intelligence, fleet consumption analysis.
Crude acquisition cost analysis across bilateral and trade agreements, refining margin tracking, product slate optimization based on market spreads, hedging intelligence, benchmark correlation analysis, export market demand patterns, regulatory compliance cost modeling.
Advanced analytics and pricing optimization have increased downstream revenue by up to 1.5% and lowered distribution costs by up to 15% (McKinsey).
Across the Enterprise
Some intelligence needs cross upstream, midstream, and downstream: workforce management, finance, IT operations, and land & royalties. Lumina agents serve these functions with the same depth.
Shift optimization, crew effectiveness, training needs analysis, turnover impact, fatigue management, competency intelligence, production revenue tracking across sites.
Royalty tracking, cost allocation, budget variance, AFE analysis, JV accounting intelligence, production revenue tracking, Vendor Audit.
Log analysis, SCADA management, license optimization, helpdesk pattern analysis, infrastructure cost tracking, regulatory filing intelligence, agreement tracking, system reliability.
Prior art search, patent management, mineral rights analysis, regulatory filing intelligence, agreement tracking, system reliability.
Deployment
Five deployment levels from public cloud to fully air-gapped. Bring your own LLM or use ours. Export agents to operate inside Claude, Copilot, or any MCP-compatible tool.
See how Lumina reasons on real operational data. Walk through a scenario with our team, or start exploring on your own.
The six-layer architecture behind how engines turn raw data into insight, learning, memory, and trust.
Deep DiveHow engineers own agents, own their reasoning and tools, run them locally, and build institutional knowledge that outlasts any individual.
InsightThe difference between displaying information and reasoning about it. And why AI needs to close the gap between data and action.
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