Background 

A global manufacturing leader in North America wanted to push the boundaries of sentiment analysis to better understand customer feedback across diverse product lines. The company sought an advanced Retrieval-Augmented Generation (RAG) solution that could deliver higher retrieval accuracy, contextual multi-turn interactions, and enhanced user experience features such as data visualization and web search. 

Challenges 

The organization faced several critical issues in advancing their AI capabilities: 

• Need for Higher Precision: Existing approaches lacked the accuracy required for nuanced sentiment classification. 

• Context Limitations: Earlier chatbot solutions did not support persistent chat history, leading to fragmented user interactions. 

• Architecture Complexity: Integrating multiple environments created latency and increased operational overhead. 

• Hallucination Risks: Lack of hybrid search and judge loops left responses vulnerable to incompleteness and inaccuracies. 

• Cost Pressures: Middleware and additional storage components introduced higher recurring costs. 

Solution 

Narwal designed and implemented a ChatGPT Enterprise RAG Path combining Custom GPT, Azure Functions, and Snowflake to meet enterprise-grade requirements: 

• Advanced Architecture 

• Built an end-to-end RAG pipeline with Custom GPT UI, Azure middleware, and Snowflake backend for robust orchestration. 

• High-Precision Embeddings 

• Leveraged text-embedding-003-large with measured retrieval precision of ~86%, significantly improving accuracy in sentiment recognition. 

• Session Management & Contextual Conversations 

• Enabled persistent chat history to support multi-turn interactions, delivering a more natural and context-aware user experience. 

• Add-On Capabilities 

• Integrated data visualization and web search to enrich the analysis and extend insights beyond text. 

• Evaluation & Benchmarking 

• Deployed the MLQA regimen (Precision@K, LLM-as-a-Judge, consistency checks) for parity comparison with alternative RAG paths. 

Outcomes 

The ChatGPT Enterprise path delivered substantial benefits: 

• Superior Retrieval Precision: Achieved ~86% precision with text-embedding-003-large. 

• Reliable Responses: Improved consistency and completeness, with ~87% measured reliability in outputs. 

• Enhanced User Experience: Multi-turn context and visualization/web search features provided richer engagement. 

• Comprehensive RAG Pipeline: Delivered a full production-ready path validated against enterprise-grade MLQA benchmarks. 

• Trade-Offs Noted: Higher latency (~30.6s) due to multi-environment flow, with added operational cost from middleware and storage. 

Conclusion 

By implementing the ChatGPT Enterprise RAG path, Narwal empowered the client with high-precision sentiment analysis and richer conversational experiences, while maintaining enterprise scalability. Despite the trade-offs of higher latency and added complexity, the solution demonstrated superior accuracy and user-centric enhancements, equipping the organization with actionable intelligence to drive better decisions and customer engagement. 

Partner with Narwal today to unlock precision, context, and scale in your AI-powered sentiment analysis journey.