# Vimeo's AI Help Desk Implementation Case Study ## Project Overview Vimeo developed a sophisticated AI-powered help desk system to enhance their customer support capabilities. The project serves as both a practical solution and a testbed for evaluating AI capabilities in production. The system was designed to provide immediate, accurate responses to customer queries by leveraging existing help center content through modern LLMOps practices. ## Technical Architecture ### Vector Store Implementation - Used vector embeddings to store and retrieve Zendesk help articles - Implemented document chunking strategy using HTML tags as delimiters - Stored metadata (URLs, titles, tags) alongside embeddings for enhanced retrieval - Used HNSWLib as the vector store for the initial prototype - Implemented webhook system from Zendesk for real-time content updates ### LLM Integration - Utilized Langchain's ConversationalRetrievalQAChain for orchestrating the chat flow - Implemented context-aware question reformulation using chat history - Integrated multiple LLM providers: ### Production Architecture Considerations - Implemented authentication using Google Cloud Platform's Workload Identity for Kubernetes deployments - Designed for model switching capability to handle different query types - Built system redundancy through multiple AI provider support - Created flexible vector store architecture supporting multiple documentation sources ## Model Evaluation and Comparison ### Performance Analysis - Google Vertex AI Chat Bison: - OpenAI Models: - Azure OpenAI: ### Cost Analysis - Vertex AI: $0.0005 per 1,000 characters (input/output) - GPT-3.5 Turbo: 0.0015/1,000 tokens (input), 0.002/1,000 tokens (output) - Consideration of token-to-character ratio in actual cost calculations ## Quality Assurance and Safety ### Quality Control Measures - Temperature parameter optimization (set to 0) for consistent responses - Implementation of detailed instruction prompts - Source URL verification and tracking - Handling of training data discrepancies ### Safety Implementation - Vertex AI safety filters for harmful content detection - Prompt restrictions for Vimeo-specific content - Response moderation for inappropriate queries - Integration with provider-specific safety features ## Technical Challenges and Solutions ### Data Management - Training data overlap detection and handling - Implementation of metadata tagging for source verification - URL management and validation system ### Response Quality - Temperature parameter optimization - Prompt engineering for consistent outputs - Implementation of response validation mechanisms ### System Integration - Webhook implementation for real-time content updates - Authentication handling across different providers - Error handling and fallback mechanisms ## Production Considerations ### Scalability - Vector store design for growing content - Multiple AI provider support - Kubernetes deployment ready ### Monitoring and Maintenance - Content update tracking - Response quality monitoring - Cost tracking across providers - Performance metrics collection ### Security - Authentication implementation - Data privacy considerations - Provider-specific security features utilization ## Future Developments ### Planned Improvements - Enhanced response validation mechanisms - Expanded content source integration - Advanced monitoring systems - User feedback integration ### Architectural Evolution - Potential multi-model approach - Enhanced context handling - Improved response generation strategies The implementation demonstrates a comprehensive approach to LLMOps, incorporating best practices in model deployment, evaluation, and maintenance while addressing real-world challenges in a production environment. The system's flexible architecture and thorough evaluation process provide valuable insights for similar implementations in enterprise environments.