Autonomous RAG-Based AI Customer Support

Autonomous RAG-Based AI Customer Support System in n8n

I designed and built an Autonomous RAG (Retrieval-Augmented Generation) AI Customer Support System using n8n, OpenAI, and Supabase to help businesses automate customer support with accurate, knowledge-based responses.

The system is trained on the company's own documentation, enabling it to understand business-specific information and provide fast, context-aware answers without requiring human intervention.

Key features include:

Importing and processing knowledge from business documents.

Splitting and indexing content into a vector database for efficient retrieval.

Using Retrieval-Augmented Generation (RAG) to fetch the most relevant information before generating responses.

Delivering accurate, AI-powered answers based on company documentation rather than generic AI knowledge.

Reducing repetitive support tasks while maintaining consistent, high-quality customer interactions.

This solution is ideal for businesses with extensive documentation, including product guides, knowledge bases, standard operating procedures (SOPs), internal manuals, FAQs, and company policies. By automating knowledge retrieval and response generation, the system improves response times, enhances customer satisfaction, and allows support teams to focus on more complex customer issues.

Built with n8n, OpenAI, Supabase, and Retrieval-Augmented Generation (RAG) to create a scalable, intelligent customer support experience.