{"id":47,"date":"2026-04-22T09:00:42","date_gmt":"2026-04-22T09:00:42","guid":{"rendered":"https:\/\/fleurendoua.com\/?page_id=47"},"modified":"2026-04-23T12:11:02","modified_gmt":"2026-04-23T12:11:02","slug":"ai-customer-support-agent","status":"publish","type":"page","link":"https:\/\/fleurendoua.com\/index.php\/ai-customer-support-agent\/","title":{"rendered":"AI Customer Support Agent"},"content":{"rendered":"\t\t
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AI Customer Support Agent: RAG-Powered Automation<\/h2>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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The context<\/span><\/h3>\n<\/div>\n
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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 \u00ab\u00a0hallucinating\u00a0\u00bb facts. I built a custom Retrieval-Augmented Generation (RAG) system using n8n to bridge the gap between static FAQ documentation and dynamic customer interactions.<\/span><\/p>\n<\/div>\n

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The purpose<\/span><\/h3>\n<\/div>\n
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The goal was to create a reliable, low-latency automated agent capable of:<\/p>\n

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    Factual Accuracy: Providing answers based strictly on a private CSV-based knowledge base.<\/p>\n<\/li>\n

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    Intelligent Escalation: Detecting negative sentiment or low-confidence responses to automatically create tickets in Zendesk.<\/p>\n<\/li>\n

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    Operational Efficiency: reducing manual agent workload by resolving common queries (e.g., \u00ab\u00a0What is Dayuse?\u00a0\u00bb) without human intervention.<\/p>\n<\/li>\n<\/ul>\n<\/div>\n

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    The results<\/span><\/h3>\n<\/div>\n
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      Zero Hallucination Rate: Successfully restricted the AI to official documentation, ensuring 100% brand-safe responses.<\/p>\n<\/li>\n

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      Seamless Ticketing: Automated the creation of detailed Zendesk tickets, including full conversation logs and sentiment analysis for human agents.<\/p>\n<\/li>\n

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      Scalability: Built a flexible architecture that can update its knowledge base in real-time simply by modifying a Google Drive file.<\/p>\n<\/li>\n

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      Cost Optimization: Transformed an inefficient 91-call process into a streamlined 1-call execution.<\/p>\n<\/li>\n<\/ul>\n<\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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      My role\u00a0<\/span><\/h3>\n<\/div>\n
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      As the Automation specialist, I was responsible for the end-to-end development of the workflow:<\/p>\n

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      • \n

        System Architecture: Designed the multi-branch logic in n8n, connecting Webhooks, AI Agents, and external APIs (Zendesk, Google Drive).<\/p>\n<\/li>\n

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        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%.<\/p>\n<\/li>\n

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        AI Orchestration: Configured LangChain agents with specific \u00ab\u00a0guardrail\u00a0\u00bb prompts to prevent misinformation.<\/p>\n<\/li>\n

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        Debugging: Resolved complex JSON issues and asynchronous data persistence problems<\/p>\n<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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        Core Logic<\/span><\/h4>\n<\/div>\n
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          Orchestration: n8n<\/span><\/p>\n<\/li>\n

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          LLMs: OpenAI (GPT-4o \/ GPT-5 Nano)<\/span><\/p>\n<\/li>\n

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          Data Sources: Google Drive (CSV FAQ), Google Sheets<\/span><\/p>\n<\/li>\n

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          Ticketing\/CRM: Zendesk API<\/span><\/p>\n<\/li>\n<\/ul>\n<\/div>\n

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          Workflow<\/span><\/h3>\n<\/div>\n
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          1. \n

            Ingestion: Receives customer queries and metadata via Webhook.<\/span>
            \u00a0<\/p>\n<\/li>\n

          2. \n

            Contextualization: Fetches and transforms FAQ data from Google Drive into a searchable knowledge base.<\/span>
            \u00a0<\/p>\n<\/li>\n

          3. \n

            Processing:<\/span><\/p>\n

            Sentiment Analysis: Categorizes user emotion (Positive\/Neutral\/Negative).<\/span>RAG Agent: Generates answers based strictly on the FAQ context.<\/span><\/p>\n

            Confidence Scoring: Programmatically evaluates the AI’s answer quality.<\/span>
            \u00a0<\/p>\n<\/li>\n

          4. \n

            Action:<\/span><\/p>\n

            Success: Delivers the verified answer back to the chat interface.<\/span><\/p>\n

            Escalation: If confidence is low or sentiment is negative, creates a Zendesk Ticket with full conversation logs.<\/span><\/p>\n<\/li>\n<\/ol>\n<\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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            AI Customer Support Agent: RAG-Powered Automation The context The project was developed to modernize customer service for a travel-tech environment 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