Love Without Sound represents an innovative case study in applying AI and LLMs to solve complex business problems in the music industry. Founded by Jordan Davis in 2023, the company has developed a comprehensive suite of NLP tools to address the critical issues of music metadata standardization and royalty recovery. This case study demonstrates how modern AI tools and LLMOps practices can be effectively deployed in a highly regulated industry requiring strict data privacy. The core business problem revolves around the music industry's metadata crisis, where incorrect or inconsistent track information leads to billions in unallocated royalties. Love Without Sound's solution architecture showcases several key aspects of production LLM systems: **System Architecture and Components:** The solution consists of multiple specialized components running in a modular architecture: * A core metadata standardization pipeline using spaCy for Named Entity Recognition (NER) and text classification, processing a 2-billion-row database * Legal document processing pipelines for analyzing correspondence and contracts * A case citation detection system with recommendation capabilities * A work-in-progress RAG system for natural language querying of case history * An audio embedding model for content-based similarity detection **Data Privacy and Production Deployment:** The system demonstrates several important LLMOps considerations: * All models run locally in a data-private environment due to the confidential nature of legal and financial information * Real-time processing capabilities for email and attachment analysis * Modular architecture allowing independent scaling and updating of components * Use of Modal for serverless deployment and scaling of both CPU and GPU workloads **Model Development and Training Pipeline:** The development process shows strong LLMOps practices: * Iterative model development using Prodigy for efficient data annotation * Continuous model improvement through active learning * Clear evaluation metrics for each component (detailed accuracy scores provided) * Separate training and deployment paths for different model components **Performance and Evaluation:** The system achieves impressive performance metrics across its components: * Metadata extraction components achieve 93-94% F-score accuracy * Email classification reaches 98% accuracy * Legal document processing components maintain 90-98% accuracy across different tasks * All components are optimized for speed, with most processing thousands of words per second **RAG Implementation and LLM Integration:** The case study describes an interesting approach to RAG implementation: * Focus on using smaller, specialized LLMs for specific tasks * LLM primarily used for natural language to SQL translation * On-premise deployment preference for better data privacy * Integration with traditional NLP pipelines **Innovative Technical Approaches:** The system showcases several innovative technical solutions: * Use of hierarchical IDs to group related song versions * Combination of transformer and CNN-based components * Integration of audio embedding models with text-based systems * Custom RAG pipeline combining SQL and natural language querying **Data Management and Privacy:** The case study demonstrates strong data handling practices: * Strict privacy controls for sensitive artist and business information * Real-time processing of incoming data * Efficient handling of large-scale metadata (billions of tracks) * Structured approach to data cleaning and standardization **Future Developments:** The case study also outlines promising future directions: * Development of audio embedding models for content-based similarity * Expansion of RAG capabilities for improved information retrieval * Enhanced automation of legal processing workflows **Technical Challenges and Solutions:** The implementation addresses several complex technical challenges: * Handling inconsistent metadata formats * Processing multiple document types (emails, PDFs, contracts) * Real-time classification and extraction * Scaling to billions of records while maintaining performance This case study demonstrates how modern LLMOps practices can be effectively implemented in a production environment while maintaining high performance and strict data privacy. The modular architecture, clear evaluation metrics, and focus on specific business problems make this an excellent example of practical AI deployment. The system's success in helping recover millions in revenue for artists while maintaining fast processing speeds and high accuracy demonstrates the real-world value of well-implemented LLMOps practices.