🚀 Project Update: AI Customer Support & Ticket Management System | n8n I’ve built and tested an AI-powered Customer Support & Ticket Management System using n8n, AI, Google Sheets, and Gmail. The goal was to automate key parts of the customer support process — from collecting customer issues and managing tickets to updating ticket statuses and handling error notifications. 🔹 1. AI Customer Support & Ticket Management The main workflow processes customer support submissions and helps organize support requests. Key features: - Customer support form for collecting issue details - AI-based ticket analysis and categorization - Ticket priority and sentiment assessment - Ticket ID generation and structured ticket records - Google Sheets integration for ticket tracking - Automated email notifications and escalation logic Status: Tested and working. 🔹 2. AI Customer Support — Ticket Status Management I built a separate workflow to manage existing support tickets. Key features: - Form-based ticket status updates - Ticket lookup using Ticket ID - Google Sheets integration to find and update records - Handling invalid or missing Ticket IDs - Support for statuses such as New, In Progress, Resolved, and Closed - Last-updated timestamp management Status: Tested and working. 🔹 3. AI Customer Support — Error Alerts I also created a separate workflow to handle error notifications. Key features: - Error alert workflow configuration - Gmail integration for sending error notification emails - Test execution to verify that an alert email can be received Status: Partially tested. The test email was received successfully, but verifying that the alert contains the actual workflow name, failed node, real error message, and correct execution details is still pending. 🧪 Testing & Debugging Testing these workflows helped me understand the importance of checking the complete automation, not just individual nodes. I focused on validating ticket processing, CRM updates, ticket status changes, invalid Ticket ID handling, and email notifications.

🚀 Built a Speed-to-Lead Voice Agent & Follow-Up System in n8n I’ve been building a Speed-to-Lead Voice Agent workflow in n8n with one main goal: Take a new lead conversation → extract the important information → qualify the lead → route it based on intent → store everything in a CRM → automatically follow up → detect replies → update the CRM. I wanted to go beyond simply creating an AI agent and build an actual business workflow around the lead. Here’s the complete build 👇 1️⃣ Webhook — Incoming Lead Trigger The workflow starts with an n8n Webhook Trigger using a POST request. The Webhook acts as the entry point for a new lead/call transcript. For testing, I used realistic lead conversations containing information such as: - Name - Phone - Email - Company - Service needed - Business requirement - Budget - Timeline - Lead conversation/transcript Example scenario: A real estate business receives leads from Facebook and Instagram and wants an AI system to respond to and manage those leads. Once the POST request reaches the Webhook, the automation starts processing the lead. 2️⃣ Prepare Lead Data After the Webhook, the data goes into a preparation step: Prepare Lead Data The purpose is to organize the incoming information and prepare it for the AI extraction stage. I wanted the downstream nodes to receive consistent lead information instead of dealing with an unstructured incoming payload. 3️⃣ AI Information Extraction The prepared lead data is then passed to an AI-powered information extraction step. The AI takes the conversation/transcript and converts the unstructured information into structured fields. The workflow extracts information such as: - Lead ID - Name - Phone - Email - Company - Service - Requirement - Budget - Timeline - Lead Score - Lead Temperature - Buying Intent - Qualification Reason - Call Summary - Transcript This is where the conversation starts becoming usable CRM data. 4️⃣ Lead Qualification Once the information is extracted, the lead is qualified based on the information available in the conversation.

I’ve just completed my AI Website Lead Generation & Qualification Chatbot built with n8n, AI Agent, Groq, Simple Memory, Structured Output Parser, and Google Sheets. The goal was to build a chatbot that can interact with website visitors, understand their business requirements, collect lead information, qualify the lead, and store/update the lead in a Google Sheets CRM. 🔹 What it does The AI collects and structures information such as: - Name & Email - Company / Business Name - Business Type - Lead Source - Service Interested In - Business Challenge - Current Process - Lead Volume - Budget - Timeline - Lead Temperature - Buying Intent - Qualification Status I also implemented a one-question-at-a-time rule, so the AI doesn't overwhelm the visitor with multiple questions in a single response. 🧠 Lead Qualification The system qualifies leads based on: Name + Email + Company + Business Challenge + Timeline It also classifies leads as: 🔥 Hot 🟡 Warm 🔵 Cold and determines their Buying Intent as High, Medium, or Low. 🔄 Lead Database Logic I connected the chatbot to Google Sheets as a simple CRM. The workflow checks whether the lead already exists using the conversation's Session ID / Lead ID. Existing lead: Get Row → IF → Merge → Update Row New lead: Get Row → IF → Append Row This allows the workflow to update existing conversations instead of creating duplicate records within the same session. 🛡️ AI Guardrails I spent a lot of time improving the prompt so the AI: - Doesn't guess missing information - Doesn't invent company names or lead sources - Preserves the visitor's actual budget and lead volume - Doesn't confuse business type with company name - Doesn't make unsupported claims about integrations or capabilities - Asks only one question at a time 📚 Biggest Learning One of my biggest takeaways from this build is that an AI Agent is much more than just connecting an LLM to a workflow. The prompt, memory, structured output, data mapping, qualification logic, database logic, and conditional branches all have to work together.

Hey everyone, A few days ago we posted the video of Josh and Jamie walking into a local business and booking a meeting in 20 minutes. The obvious question after watching it: "Sure, but does that actually turn into clients?" Briland just answered it (screenshot attached) He's one of our Accelerator members, and this week he posted that he closed 3 deals in the past week. All three came from getting out there in person: networking events and local mastermind groups. No cold email. No content machine. Three different industries too. Which tells you it wasn't a niche thing or a lucky room. His words: "There is something to be said about getting out there to make your name known as the AI guy in your area." Now there is a gap between the video and Briland's result. The video shows you how to get in a room. Turning that meeting into a signed deal is a skill, and it's the exact thing Jamie (the guy from the video) and the sales coaches work on with members inside the Accelerator every week. So if you're at the point where you want help turning these conversations into clients: → Map the gap with my team [HERE] Briland's week didn't come from a lucky room. It came from knowing what to do once he was in one. If that's the gap for you, the call is where we map how to close it. Talk soon.

Hey, question. You still watching the classroom videos? Still trying to work out where to start? If not, ignore the rest of this. If yes, I'll be straight with you. If you still haven't signed a client, or have hit a roadblock in scaling the agency, speak with my team for clarity & guidance. Call slots are usually scarce so I've opened more this week. It's Tuesday. By Friday you could have a clear view of your next 3 moves. [BOOK A FREE CONSULTING CALL] The people who win aren't the smartest. They're the ones who take action. I've seen too many comments this week asking where to start and what to do. So if it's unclear and you want that to change, book the call and get an answer. My team's track record: - $18M+ in client generated revenue - $27k+ average revenue per client - 67 days average to first client - 3+ years operating You make the call. Liam.