The Challenge
Our client operates a high-volume customer service center receiving hundreds of inquiries daily. The goal was to have an AI-powered chatbot handle frequently asked questions, so human operators could focus on more complex cases.
Our Approach
1. Data Assessment and Knowledge Base Building
The first step was analyzing the existing FAQ database and previous customer service conversations. From this, we built a structured knowledge base stored in vector format (Pinecone).
2. RAG Architecture
We chose the Retrieval-Augmented Generation (RAG) approach: based on the user's question, the system first retrieves relevant documents, then passes them as context to the GPT model.
3. Fine-tuning and Guardrails
We ensured response quality through prompt engineering and a guardrails system – the chatbot only responds based on the approved knowledge base, and redirects to a human operator when necessary.
Results
- 68% reduction in inquiries reaching human operators
- 4.2/5 user satisfaction score
- < 2 sec average response time
Lessons Learned
An AI chatbot doesn't replace customer service – it complements it. The key to success was a high-quality knowledge base and continuous fine-tuning.
If you're considering a similar solution, get in touch with us!