This case study, presented jointly by TCS and MongoDB, explores the implementation of a modern data platform that combines Operational Data Layers (ODLs) with generative AI capabilities. The focus is on creating a unified system that can handle both real-time and batch processing while leveraging advanced AI features for enhanced data operations. The core challenge addressed is the widespread issue of organizations struggling with legacy systems and fragmented data architectures, particularly in sectors still relying on decades-old mainframe systems. These outdated systems create significant operational inefficiencies and prevent organizations from delivering real-time, personalized services that modern customers expect. The technical implementation centers around three main components: * Operational Data Layer (ODL): Serves as the foundational architecture, acting as a centralized hub that integrates data from multiple transactional systems in real-time. The ODL is implemented using MongoDB as the underlying database, chosen for its flexibility and ability to handle diverse data types and workloads. * Generative AI Integration: The platform incorporates generative AI capabilities, particularly through Retrieval Augmented Generation (RAG) implementation. The case study specifically mentions how the converged AI data store provides context to the LLM prompts, enabling more accurate and contextually relevant AI responses. * Vector Search Capabilities: The platform implements vector search technology to handle high-dimensional data, including text, images, and audio, enabling more sophisticated search and retrieval operations. The architecture supports both Lambda and Kappa patterns for data processing: The Lambda architecture implementation consists of three layers: * A batch layer for processing large volumes of historical data, enhanced with generative AI for insight generation * A speed layer handling real-time data streams * A serving layer that unifies both batch and real-time data views The Kappa architecture implementation focuses on real-time analytics using a single stream processing approach, with MongoDB serving as the operational speed layer. From an LLMOps perspective, several key aspects are worth noting: * Data Integration for AI: The platform demonstrates sophisticated handling of both structured and unstructured data, essential for training and operating LLMs effectively. The ODL serves as a unified source of truth, ensuring that AI models have access to consistent, up-to-date data. * RAG Implementation: The case study specifically details how RAG is implemented using the converged AI data store, showing a practical approach to combining enterprise data with LLM capabilities. This implementation allows for context-aware AI responses while maintaining data accuracy and relevance. * Real-time AI Operations: The architecture supports real-time AI operations through its speed layer, enabling immediate response generation and continuous model updates based on fresh data. * Vector Search Integration: The implementation of vector search capabilities shows how the platform handles embedding-based operations, crucial for modern LLM applications. This enables semantic search and similarity matching across different data types. The platform's progression follows a clear modernization journey: * Starting with a basic operational data store * Evolving to an enriched ODL with real-time analytics * Implementing parallel writes for enhanced performance * Transitioning to a microservices-based architecture * Finally becoming a full system of record Security and governance considerations are built into the platform, with particular attention to handling sensitive data and maintaining compliance with regulations like GDPR and PCI DSS. This is crucial for LLM operations in regulated industries. The case study also highlights several practical applications: * Customer service improvements through real-time data access and AI-powered interactions * Automated compliance reporting using gen AI to process and summarize data from multiple sources * Personalized recommendation systems leveraging historical data and real-time customer behavior * Enhanced search capabilities across multiple data modalities While the case study presents a comprehensive approach to modernizing data infrastructure with AI capabilities, it's important to note that some of the claims about performance improvements and cost reductions (such as the mentioned 50% cost reduction) should be viewed as potential outcomes rather than guaranteed results, as they would likely vary significantly based on specific implementation details and organizational contexts. The implementation demonstrates a practical approach to operationalizing LLMs in an enterprise context, showing how to combine traditional data infrastructure with modern AI capabilities while maintaining operational efficiency and data governance. The emphasis on real-time processing and data integration provides a solid foundation for building sophisticated AI-powered applications that can scale with business needs.