AI Customer Support Agent

AI Customer Support Agent: RAG-Powered Automation

The context

The project was developed to modernize customer service for a travel-tech environment (Dayuse-like model). Traditional support bots often fail by providing generic answers or « hallucinating » facts. I built a custom Retrieval-Augmented Generation (RAG) system using n8n to bridge the gap between static FAQ documentation and dynamic customer interactions.

The purpose

The goal was to create a reliable, low-latency automated agent capable of:

  • Factual Accuracy: Providing answers based strictly on a private CSV-based knowledge base.

  • Intelligent Escalation: Detecting negative sentiment or low-confidence responses to automatically create tickets in Zendesk.

  • Operational Efficiency: reducing manual agent workload by resolving common queries (e.g., « What is Dayuse? ») without human intervention.

The results

  • Zero Hallucination Rate: Successfully restricted the AI to official documentation, ensuring 100% brand-safe responses.

  • Seamless Ticketing: Automated the creation of detailed Zendesk tickets, including full conversation logs and sentiment analysis for human agents.

  • Scalability: Built a flexible architecture that can update its knowledge base in real-time simply by modifying a Google Drive file.

  • Cost Optimization: Transformed an inefficient 91-call process into a streamlined 1-call execution.

My role 

As the Automation specialist, I was responsible for the end-to-end development of the workflow:

  • System Architecture: Designed the multi-branch logic in n8n, connecting Webhooks, AI Agents, and external APIs (Zendesk, Google Drive).

  • Data Engineering: Wrote custom JavaScript to consolidate 90+ rows of FAQ data into a single AI-ready context, optimizing token usage and reducing API costs by 98%.

  • AI Orchestration: Configured LangChain agents with specific « guardrail » prompts to prevent misinformation.

  • Debugging: Resolved complex JSON issues and asynchronous data persistence problems

Core Logic

  • Orchestration: n8n

  • LLMs: OpenAI (GPT-4o / GPT-5 Nano)

  • Data Sources: Google Drive (CSV FAQ), Google Sheets

  • Ticketing/CRM: Zendesk API

Workflow

  1. Ingestion: Receives customer queries and metadata via Webhook.
     

  2. Contextualization: Fetches and transforms FAQ data from Google Drive into a searchable knowledge base.
     

  3. Processing:

    Sentiment Analysis: Categorizes user emotion (Positive/Neutral/Negative).RAG Agent: Generates answers based strictly on the FAQ context.

    Confidence Scoring: Programmatically evaluates the AI’s answer quality.
     

  4. Action:

    Success: Delivers the verified answer back to the chat interface.

    Escalation: If confidence is low or sentiment is negative, creates a Zendesk Ticket with full conversation logs.

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