# DXC Oil & Gas Data Exploration LLMOps Case Study ## Company and Use Case Overview DXC Technology, a global IT services provider supporting 6,000 customers across 70 countries, developed an advanced AI assistant to transform data exploration for oil and gas companies. The solution addresses a critical industry challenge where data is scattered across multiple locations and formats, making efficient analysis difficult. By leveraging LLMs and specialized tools, they created a system that dramatically reduced exploration time from hours to minutes. ## Technical Architecture ### Core Components - **Router System** - **Specialized Tools** ### Integration and Data Management - Uses Amazon S3 for data storage - Implements signed S3 URLs for secure UI access - Integrates with Amazon Bedrock Knowledge Bases for document management - Supports multiple data formats including PDFs, Excel files, and industry-specific formats ## LLMOps Implementation Details ### Model Selection and Management - Primary use of Anthropic's Claude models through Amazon Bedrock - Strategic model selection based on task complexity: ### Prompt Engineering and Management - Structured prompt templates using XML formatting - Specialized prompts for each tool type - Comprehensive error handling and self-correction mechanisms - Context-aware query rewriting system for conversational capabilities ### System Architecture and Integration - Modular design with specialized tools for different data types - Centralized routing system for query classification - Integration with multiple AWS services - Scalable architecture supporting various data formats ### Conversational Capabilities - Query rewriting layer for context management - History-aware response generation - Support for follow-up questions - Translation and summarization capabilities ### Testing and Evaluation - Implementation of guardrails for non-relevant queries - Token limit management - Error handling mechanisms - Performance optimization for latency reduction ## Deployment and Production Considerations - Secure integration with existing data systems - Scalable architecture supporting multiple data sources - Implementation of access controls through signed URLs - Integration with enterprise security protocols ## Results and Impact - Significant reduction in data exploration time - Enhanced ability to analyze complex datasets - Improved decision-making capabilities for drilling operations - Substantial cost savings through faster time to first oil ## Technical Challenges and Solutions - Managing large-scale data processing - Handling multiple specialized file formats - Implementing secure data access - Optimizing response times - Building reliable query routing ## Future Improvements - Additional tool development for other data types - Enhanced SQL database integration - Automated dataset selection - Integration with Amazon Bedrock Agents - Expansion to other industry-specific formats The solution demonstrates sophisticated LLMOps practices including modular architecture, specialized tool development, proper model selection, and robust prompt engineering. The implementation shows careful consideration of production requirements including security, scalability, and performance optimization.