Customer Support Chatbot
Intelligent LLM and RAG-based chatbot for automating routine customer inquiries.
Challenge
The contact center of a major financial company processed tens of thousands of inquiries monthly, with up to 60% being routine questions. Response wait times were growing, and expanding the operator staff did not address the scalability challenge. The client sought a solution that would automate routine request handling without sacrificing quality.
Solution
We built an intelligent chatbot powered by a large language model, adapted to the client's terminology and business processes. The bot leverages an up-to-date knowledge base through RAG architecture, ensuring factual accuracy of responses. The system handles routine requests and, when necessary, routes complex inquiries to a human operator with full context handover.
Results
Technologies
Approach
Routine inquiry and knowledge base analysis
Classifying inquiries, identifying patterns, structuring the knowledge base.
LLM tuning and domain adaptation
Fine-tuning the model on the client's terminology and processes to improve relevance.
RAG pipeline development
Building a knowledge base search system with vector indexing for factual accuracy.
Testing and launch
Testing on real inquiries, configuring operator routing, production deployment.
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