Case studies, white papers, and product demos.

Case Study

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

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

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.

Case Study

Rights and Deal Intelligence Across a Major Studio's Collections

For a major media and entertainment studio, xMentium worked with licensing agreements across multiple entities, deal types, formats, and legacy collections.

xMentium defined a custom schema with up to 40 metadata fields and a human-in-the-loop quality-review workflow across licensing agreements spanning multiple entities, deal types, formats, and legacy collections.

The output provides a consistent data layer across fragmented repositories, supporting cross-entity research, rights views, dashboards, business affairs and finance workflows, and grounded AI.