# Faber Labs' Goal-Oriented Retrieval Agents for Production Scale ## Company Overview Faber Labs has developed Gora (Goal-Oriented Retrieval Agents), an innovative system designed to transform subjective relevance ranking at scale. The company focuses on providing embedded KPI optimization layers for consumer-facing businesses, particularly in e-commerce and healthcare sectors. ## Technical Architecture and Implementation ### Core Components - Goal-Oriented Architecture - Real-time Processing System ### Technology Stack - Backend Implementation - Model Architecture ### Performance Optimization - Latency Management - Scaling Considerations ## Implementation Challenges and Solutions ### Privacy and Security - Development of on-premise solutions - Privacy-preserving learning across clients - Secure handling of sensitive medical and financial data - Implementation of Large Event Models for data generalization ### Technical Hurdles - Transition challenges from Python/Scala to Rust - Balance between personalization and privacy - Management of conversation context at scale - Integration of real-time feedback systems ### Performance Requirements - Sub-3-second response time target (based on 53% mobile user abandonment data) - Optimization for conversation-aware and context-aware modeling - Efficient handling of follow-up prompts - Scalable infrastructure for multiple client support ## Results and Impact ### Performance Metrics - Significant improvement in load times - Position ahead of industry benchmarks for conversational systems - Consistent sub-second response times for complex queries - Scalable performance across different client needs ### Business Impact - Over 200% improvement in both conversion rates and average order value - Successful deployment across multiple industries - High client satisfaction rates - Demonstrated effectiveness in both e-commerce and healthcare sectors ## Technical Infrastructure ### Data Processing - Ability to handle messy client data - Real-time processing capabilities - Efficient caching mechanisms - Privacy-preserving data handling ### Model Development - Custom Large Event Models - Integration with open-source LLMs - Reinforcement learning optimization - Adaptive learning systems ## Future-Proofing - Architecture designed for model upgrades - Ability to incorporate new frameworks - Scalable infrastructure - Flexible deployment options ## Key Innovations ### Technical Advances - Development of Large Event Models - Implementation of unified goal optimization - High-performance Rust backend - Real-time feedback processing system ### Business Implementation - Cross-industry applicability - Privacy-preserving learning - Scalable deployment options - Measurable business impact ## Lessons Learned ### Technical Insights - Value of Rust in production systems - Importance of unified goal optimization - Benefits of real-time processing - Significance of privacy-first design ### Operational Learnings - Importance of latency optimization - Value of cross-client learning - Need for flexible deployment options - Balance between performance and privacy