1.0https://fleurendoua.comadmin8805https://fleurendoua.com/index.php/author/admin8805/rich600338<blockquote class="wp-embedded-content" data-secret="ZrkpiTfLy2"><a href="https://fleurendoua.com/index.php/ai-customer-support-agent/">AI Customer Support Agent</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://fleurendoua.com/index.php/ai-customer-support-agent/embed/#?secret=ZrkpiTfLy2" width="600" height="338" title="« AI Customer Support Agent » — " data-secret="ZrkpiTfLy2" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script> /*! This file is auto-generated */ !function(d,l){"use strict";l.querySelector&&d.addEventListener&&"undefined"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!/[^a-zA-Z0-9]/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret="'+t.secret+'"]'),o=l.querySelectorAll('blockquote[data-secret="'+t.secret+'"]'),c=new RegExp("^https?:$","i"),i=0;i<o.length;i++)o[i].style.display="none";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute("style"),"height"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):"link"===t.message&&(r=new URL(s.getAttribute("src")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener("message",d.wp.receiveEmbedMessage,!1),l.addEventListener("DOMContentLoaded",function(){for(var e,t,s=l.querySelectorAll("iframe.wp-embedded-content"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute("data-secret"))||(t=Math.random().toString(36).substring(2,12),e.src+="#?secret="+t,e.setAttribute("data-secret",t)),e.contentWindow.postMessage({message:"ready",secret:t},"*")},!1)))}(window,document); //# sourceURL=https://fleurendoua.com/wp-includes/js/wp-embed.min.js </script> 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 Ingestion: Receives customer queries and metadata via Webhook.  Contextualization: Fetches and transforms FAQ data from Google Drive into a searchable knowledge base.  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.  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. PHONE +33667368654 EMAIL allua.ndoua@sciencespo.fr  FOLLOW ME Linkedinhttp://fleurendoua.com/wp-content/uploads/2026/04/af2fc5_e384dc57d5d54e4e89a7ecd1de481a54mv2.png