## Company Overview and Business Challenge Tyson Foods operates as one of the largest protein providers in the United States, producing approximately 20% of the nation's beef, pork, and chicken. Their Tyson Foodservice division operates through a B2B model, selling products to distributors rather than directly to end consumers, and serves diverse foodservice operators including restaurants, schools, healthcare facilities, and convenience stores. The company faced a significant challenge in that they had limited direct engagement with over 1 million unattended operators who purchased their products through distributors without direct company relationships. This gap represented a substantial missed opportunity for customer insights, direct communication, and scaled sales efforts. The traditional approach to product search on their website relied on keyword-based matching, which created friction for foodservice professionals who often use industry terminology that varies from official catalog descriptions. For example, chefs searching for "pulled chicken" might miss relevant products labeled as "shredded chicken," or those looking for "wings" might not see results for "party wings" or "drummettes." This disconnect was particularly problematic for food service professionals working under tight deadlines, ultimately driving them to competitors where they could more quickly find what they needed. ## Technical Architecture and LLMOps Implementation Tyson Foods collaborated with the AWS Generative AI Innovation Center to develop a comprehensive AI-powered solution that addresses both semantic search and conversational interaction challenges. The architecture demonstrates sophisticated LLMOps practices across multiple components deployed in production. ### Semantic Search Infrastructure The semantic search component represents a significant upgrade from traditional keyword matching. The system uses Amazon OpenSearch Serverless as the vector database, which provides automatic scaling of compute and storage resources based on query volume and product catalog size. This serverless approach minimizes operational overhead while maintaining cost-efficiency through usage-based pricing. The preprocessing workflow implements sophisticated content curation using large language models to transform raw product metadata into optimized semantic search queries. Rather than indexing all content verbatim, the system uses LLMs to analyze and extract only the most relevant elements from each content piece, creating meaningful search strings specifically designed for semantic indexing. This approach filters out presentational website copy and non-essential informational text while emphasizing search-critical elements like culinary applications, preparation methods, and ingredient specifications. The system uses Amazon Titan Text Embeddings V2 model on Amazon Bedrock to convert processed content into vector representations. The search application is deployed on Amazon Elastic Container Service (Amazon ECS) using AWS Fargate as the capacity provider, exposed as a REST API through an Application Load Balancer protected by AWS WAF. This architecture demonstrates production-ready deployment practices with proper security and scalability considerations. A particularly noteworthy aspect of the preprocessing is the prompt engineering approach used to optimize content for semantic indexing. The system employs a detailed prompt that instructs the LLM to create concise search strings focusing on distinguishing features, concrete attributes, and specific selling points while avoiding generic marketing terms. The prompt includes specific guidelines for handling brand names, product types, preparation states, and variety assortments, demonstrating sophisticated prompt engineering practices for production use. ### Agentic Conversational Interface The conversational AI component showcases advanced agentic system design using Anthropic's Claude 3.5 Sonnet on Amazon Bedrock combined with LangGraph for orchestration. The architecture implements a multi-agent system with distinct roles and responsibilities, representing state-of-the-art practices in production LLM deployment. The core architecture consists of three main components: an agent node that handles conversational interaction and decision-making, a tool execution node that manages function calls, and a tools layer that implements specific business functions. The agent node uses the tool calling capabilities of Claude 3.5 Sonnet to implement agentic behavior, allowing the system to dynamically choose appropriate tools based on user queries and maintain conversational context across interactions. LangGraph provides the orchestration framework, offering primitives specifically designed for building agentic systems with LLMs. The framework handles the complex state management required for multi-turn conversations while maintaining the ability to execute tools and return results in a coherent conversational flow. This demonstrates sophisticated production deployment of agentic systems that can handle complex, multi-step user requests. The system prompt for the agent node is comprehensive and well-structured, defining the assistant's role, operational boundaries, and interaction patterns. The prompt includes detailed guidelines for tool usage, result presentation, and customer service protocols, showing mature prompt engineering practices for production conversational AI systems. The prompt also includes specific formatting instructions for different types of content (products, recipes, articles) and handling of various customer scenarios. ### Tool Integration and Business Logic The tools layer implements stateless functions that augment the LLM's capabilities by connecting to various business systems and databases. Tools include semantic search capabilities, product detail retrieval, distributor location services, purchasing assistance, promotion awareness, and feedback collection. Each tool is designed as a thin wrapper around services and database layers, maintaining clean separation of concerns and enabling easy maintenance and updates. The system demonstrates sophisticated handling of business logic through the agentic approach. For example, when users inquire about purchasing products, the agent can present multiple options (distributor purchase vs. direct sales contact), help users identify preferred distributors, check product availability, and provide appropriate ordering information. This level of business process integration while maintaining conversational flow represents advanced LLMOps implementation. ### Production Deployment and Scalability The deployment architecture shows production-ready considerations with Amazon ECS clusters using Fargate for both the search application and AI assistant components. The use of Application Load Balancers with AWS WAF protection demonstrates proper security practices for production AI applications. The separation of ingestion processes into different ECS clusters shows thoughtful resource management and scaling considerations. Amazon RDS database clusters are used to persist high-value user actions for analytics purposes, creating a feedback loop that transforms customer interactions into strategic business intelligence. This approach goes beyond traditional web analytics by capturing rich conversational data that provides deeper insights into customer interests, pain points, and purchase intentions. ### High-Value Action Capture and Business Intelligence One of the most innovative aspects of the implementation is the high-value action capture mechanism, which represents advanced thinking about LLMOps in production environments. Rather than analyzing chat logs after the fact, the system integrates insight collection directly into the AI assistant's operational workflow through LangGraph. When specific tools are invoked to fulfill user requests, these tool calls simultaneously trigger the capture of meaningful interaction data. This dual-purpose approach ensures that valuable business intelligence is gathered as a natural byproduct of providing customer service, without requiring additional processing or analysis steps. The system includes configurable parameters that allow business teams to adjust which user intents and actions qualify as high value based on evolving business priorities. This demonstrates mature thinking about how LLM systems in production can provide value beyond their primary function. ### Challenges and Considerations While the case study presents impressive capabilities, several LLMOps challenges and considerations emerge from the implementation. The system requires careful prompt engineering and maintenance to ensure consistent behavior across different user scenarios and product categories. The semantic search preprocessing workflow adds complexity but provides significant value in search relevance. The agentic system design, while powerful, introduces complexity in debugging and monitoring compared to simpler chatbot implementations. The tool-calling approach requires careful error handling and fallback mechanisms to maintain conversational flow when tools fail or return unexpected results. The high-value action capture mechanism, while innovative, requires careful privacy considerations and data governance practices to ensure customer trust and regulatory compliance. The system must balance insight collection with user privacy and transparency about data usage. ### Production Monitoring and Maintenance The case study touches on but doesn't deeply detail the monitoring and maintenance aspects of the production system. Managing embeddings models, keeping product catalogs synchronized, and maintaining conversational quality across diverse user interactions represents ongoing operational challenges typical of production LLM systems. The serverless approach using OpenSearch Serverless and managed services like Amazon Bedrock reduces some operational overhead, but the complex multi-component architecture still requires sophisticated monitoring, alerting, and maintenance practices to ensure reliable performance in production. This case study demonstrates a sophisticated, production-ready implementation of multiple LLMOps practices including semantic search, agentic conversation systems, tool integration, and business intelligence capture, representing a comprehensive approach to deploying generative AI in enterprise B2B environments.