xMentium
PartnersCompanyContactLoginRequest a Demo
Oil & Gas

Actionable asset intelligence for Oil & Gas.

Transform your complex, proprietary data into enterprise intelligence to maximize uptime, mitigate risk, identify growth opportunities, and guide better strategic decisions.

Request A DemoRead Whitepaper

01 The Oil & Gas Challenge

Oil & Gas has the data. The challenge: putting it to work.

Oil & Gas companies are investing heavily in AI, but much of the intelligence needed to create value remains trapped in complex, unstructured documents. Without a reliable way to extract, structure, and contextualize that information, AI initiatives struggle to scale. That's where xMentium comes in.

88%
Companies Using AI
95%
Pilots Struggling to Produce ROI
80%
Companies Not Seeing Financial ROI
25%
Time Wasted Searching for Documents

Data Sources: MIT Project NANDA, The State of AI in Business 2025. McKinsey, The State of AI 2025 (November 2025, n=1,993 executives, 105 countries).

02 What We Do

Turn complex documents into structured, usable intelligence.

xMentium extracts high-accuracy data and insights from your proprietary documents, creating a trusted data foundation that people, applications, analytics, and AI can reliably use at scale.

Unlock complex document data

Extract critical information from scans, tables, diagrams, P&IDs, engineering drawings, and other complex content.

Create structured enterprise knowledge

Transform fragmented document content into consistent metadata, relationships, and reusable structured data.

Make proprietary intelligence usable

Turn decades of operational knowledge into decision-ready data for people, analytics, applications, and AI.

Enrich the systems you already use

Put structured intelligence to work across existing platforms and workflows, without replacing your technology stack.

03 How It Works

Purpose-built for complex Oil & Gas documents at scale.

Oil & Gas documents are complex, with critical information embedded across text, scans, tables, diagrams, drawings, and layouts. A special processing engine is required to maximize accuracy, speed, and scale and minimize cost.

01

Ingest

Connect to your documents: import PDFs, scans, images, and other formats from existing repositories.

02

Classify

Create consistent structure: automatically classify documents against your defined taxonomy.

03

Decompose & Extract

Unlock the data inside: extract tables, metadata, content, and context into structured, machine-readable data.

04

Validate & Deliver

Put trusted data to work: validate results and deliver structured data directly to the systems and AI tools that need it.

04 The Need: Market Perspective

Market leaders agree: better data means better business outcomes.

Customers across every industry and segment moved from AI experimentation to deploying AI for real-world business outcomes. The winning companies are building intelligence platforms specifically so the value of AI accrues to the customer, not the model.

Judson AlthoffCEO, Microsoft Commercial Business

The entire oil and gas industry is being forced to be more efficient. Being able to access this data immediately instead of having to go dig through different silos and try to piecemeal it has a huge impact on uptime. Now you're talking about way more revenue than the cost savings you generate.

Independent Oil & Gas Industry Expert

We have identified more than 100 ways to save millions through better turnarounds and maintenance, but we cannot act until our language data is structured with a tool like xMentium.

Senior ExecutiveOil & Gas Supermajor

You want to ensure that your actions or any generated content is backed by trusted and verifiable data sources … When it comes to AI agents, it's not about feeding them everything. It's about feeding them the right things. You want precision, not just volume.

Kathy BaxterSalesforce

The acquired company had 30 years of technical documentation and customer records in various formats and systems. xMentium helped us extract and preserve critical operational knowledge that would have taken years to manually review, preventing significant knowledge loss during the integration.

VP of Corporate DevelopmentFortune 500 Industrial Company

Pull the thread on each of these cases [for GenAI], and it will lead back to data. Your data and its underlying foundations are the determining factors to what's possible with generative AI.

McKinsey & Company

We were flying blind. xMentium gave us rapid visibility into thousands of our contracts. We found missed obligations and billing correction opportunities in days.

VP, Revenue OperationsTier 1 Telecommunications Company

Building a modern data foundation is the first investment companies must make to realize the true value of AI and gen AI.

Accenture

As open source models close the performance gap with proprietary ones, the differentiator shifts from which model you use to what data you feed it.

Stanford Digital Economy Lab

Our AI projects have failed as we don't have good foundational data. If we did data extraction manually without AI, it would take us 10 years… 800 docs per person per week.

Lead AI Project ManagerOil & Gas Supermajor

Bad inputs lead to bad outputs. Organizations need to ensure the proprietary data they're feeding to AI models is reliable and accurate … Otherwise, it can make the model perform worse.

IBM

05 How We're Different

Built for complexity. Proven at scale.

xMentium combines specialized document processing with enterprise-scale architecture to deliver trusted, structured data across even the most complex oil and gas content.

Built for complex content

Extract intelligence across scans, dense tables, engineering drawings, P&IDs, diagrams, and other multimodal documents.

The right model for every job

Orchestrate OCR, VLMs, LLMs, custom models, and deterministic methods based on the content and task.

Proven accuracy at scale

Delivers ~95% extraction accuracy while processing 100K+ documents per day, with human review where it matters.

Outputs for your full stack

Deliver structured, reusable metadata upstream so AI, analytics, and workflows downstream operate on more dependable data.

Works within your existing tech stack

xMentium is a Microsoft AI Solutions Designation–certified Document Intelligence platform. We connect with your existing repositories, extract the intelligence inside, and deliver structured data for the systems that need it.

AzureSharePointFabricPurviewCopilotPower PlatformPower BIOpenTextRightslineDatabricksGoogle CloudAmazon Web Services

06 Oil & Gas Use Cases

Put your industry's most valuable information to work.

Select a use case to explore

Maximize uptime and asset reliability

Connect intelligence across P&IDs, inspection reports, maintenance records, and operating procedures to drive predictive maintenance, turnaround planning, operational efficiency, and stronger HS&E performance.

Operations & Asset Performance

07 The Real Business Impact

From structured data to measurable business impact.

See how a strong data foundation can help oil and gas companies scale AI impact to realize efficiency gains, pursue growth and innovation, improve enterprise-level EBIT, and create durable competitive advantage.

Case Study

The AI-Ready Data Foundation Behind a $300B+ Oil & Gas Supermajor

95%
Extraction Accuracy
100M+
Documents
100K/day
Target Processing Scale
10X
Faster Human Review

A $300B+ global oil & gas supermajor selected xMentium from 119 vendors to extract, structure, and govern 100M+ enterprise documents, creating a trusted data foundation for AI, analytics, and high-value operational use cases.

Preparing for a refinery turnaround requires a clear understanding of an asset's operational history, but that history is rarely found in one place. For one global oil and gas company, critical intelligence was distributed across a repository of more than 100 million documents, including P&IDs, work packages, condition assessments, maintenance records, manuals, and inspection reports. Manually reconstructing an integrated view of asset and operational intelligence was difficult, slow, costly, and prone to inconsistencies that could undermine accuracy and confidence in downstream decisions.

By classifying documents and extracting key information including asset and equipment identifiers, dates, and other metadata, the company began turning disconnected records into structured, connected intelligence. The approach achieved 95% extraction accuracy and incorporated human-in-the-loop validation designed to make review workflows up to 10X faster. At production scale, the architecture targets processing 100,000 documents per day.

The result extends well beyond turnaround preparation. The structured, governed knowledge layer is designed to support more than 100 downstream AI and analytics use cases, including operational improvement, EPC buildouts, and field inspection quality. By creating the data foundation once and making it reusable across the enterprise, the company can turn decades of operational history into intelligence that supports both immediate workflows and future AI initiatives.

Case Study

The Conformance Automation Engine Selected by a Global EPC Leader

90–95%
Specification Accuracy
96,000
Engineering Hours Saved Annually
$42.8M
Annual Value Delivered

A global EPC leader selected xMentium from 50 vendors to automate specification matching across complex engineering documents, reducing manual review while improving accuracy and accelerating procurement workflows.

For a global EPC company, RFP conformance review was a labor-intensive process where errors or overlooked requirements could create significant downstream commercial, operational, and project-delivery risk. More than 64,000 vendor responses each year required roughly 96,000 engineer-hours of conformance review. Requirements were buried across technical specifications, while vendor responses appeared in data sheets, diagrams, tables, and text documents. Engineers had to find the relevant information and then accurately match each response to its corresponding requirement, a process that constrained throughput and left room for costly discrepancies that could lead to rework, delays, performance issues, and quality costs.

The company transformed the process by extracting and normalizing requirements from source specifications alongside corresponding information from vendor submissions. The resulting structured data created a repeatable comparison workflow, with supporting evidence and human review allowing engineers to validate requirements and responses side by side rather than building each comparison manually. The result was a process designed to improve speed, reduce cost, increase accuracy, and scale across a much larger volume of vendor submissions.

Using live customer documents, the proof of concept demonstrated 90–95% accuracy extracting information from engineering specifications and vendor drawings. Instead of engineers manually finding and comparing every requirement, the system structures the information first and presents the relevant requirement and vendor response for human validation. This allows engineers to spend more time reviewing potential discrepancies and less time gathering information. The approach is designed to scale to thousands of packages, increasing review capacity while reducing the risk that costly specification mismatches are missed. A bottom-up ROI model estimated $42.8 million in annual value from lower review costs and avoided re-procurement, rework, delays, and quality costs.

08 FAQs

Your questions, answered.

01Why can't our existing AI tools analyze our documents?

AI can only act on information it can reliably find and understand. In oil & gas, critical intelligence is often trapped inside P&IDs, inspection reports, engineering drawings, procedures, and contracts. xMentium turns that content into structured, contextualized data that AI, analytics, and operational systems can actually use.

02How can we trust AI-extracted data for critical decisions?

xMentium combines multiple extraction techniques with confidence scoring and human-in-the-loop validation, delivering greater than 95% extraction accuracy, even across complex structured and unstructured content, creating traceable, dependable data.

03Can xMentium handle complex engineering documents, not just PDFs and text?

Yes. xMentium is built for the documents that break conventional extraction approaches, including P&IDs, engineering drawings, diagrams, scans, spreadsheets, nested tables, and specifications. It applies the right combination of OCR, visual language models, LLMs, and deterministic methods.

04Can xMentium work at the scale of a major oil & gas enterprise?

Yes. xMentium is built to process millions of documents. In a competitive global evaluation, xMentium was selected over 100+ alternatives for an initiative spanning more than 100 million documents, with targeted throughput of 100,000 documents per day.

05Do we need to migrate our documents or replace our existing systems?

No. xMentium works with your existing repositories: SharePoint, OpenText, Documentum, network drives, and cloud storage. Your documents can stay where they are while xMentium makes the intelligence inside them available to AI, BI, asset registries, and digital twins.