I just finished building an end-to-end AI customer support workflow using n8n, and I’m really excited about how powerful and clean this setup turned out. 🔧 What this workflow does: 📩 Monitors Gmail in real time using a Gmail Trigger 🧠 Classifies incoming emails (Customer Support vs Other) using an LLM 🤖 Automatically generates friendly, human-like replies for support emails 📚 Uses a Pinecone vector knowledge base (FAQ & policies) for accurate answers ✍️ Responds as a branded support agent (“Mr. Helpful from Tech Haven Solutions”) 🏷️ Applies Gmail labels automatically for better inbox organization ⛔ Non-support emails are safely ignored 🛠 Tech Stack: n8n (workflow orchestration) OpenAI GPT-4o & OpenRouter (LLMs) LangChain nodes (agents, classifiers, tools) Pinecone (vector database for RAG) Gmail API (triggering & labeling) 💡 Why this matters: This setup drastically reduces manual support workload while still keeping responses: Context-aware Knowledge-grounded Brand-consistent Fast ⚡ It’s a great example of how AI agents + RAG + automation can deliver real business value—not just demos. If you’re exploring AI automation, n8n workflows, or LLM-powered customer support, I’d love to connect and exchange ideas. hashtag#AI hashtag#n8n hashtag#Automation hashtag#CustomerSupport hashtag#LLM hashtag#RAG hashtag#OpenAI hashtag#Pinecone hashtag#NoCode hashtag#LangChain hashtag#EmailAutomation

Today, I took a big step in streamlining my lead-handling process by creating a complete automation workflow in n8n — and it’s already saving hours of manual work every week. Here’s what the workflow does automatically: ✅ Captures form submissions ➡️ Stores each lead in Google Sheets (creates or updates the row) ✅ Checks the lead’s budget ➡️ If the budget is ≥ 1000, it triggers a custom email to notify me ✅ Sends personalized follow-up emails ➡️ Automatically detects the requested service (Ads or SEO) ➡️ Sends a tailored message with the client's first name ✅ Updates the CRM sheet ➡️ Marks leads as Contacted ➡️ Automatically flags Rejected when the budget doesn’t qualify All of this happens instantly — no manual work, no copy-paste, no delays. Just clean, efficient automation. ⚡ Building systems like these is exactly how businesses stay organized, respond faster, and close more deals. If you need help setting up automations, CRM flows, or AI-powered workflows… I’d love to help you streamline your processes too! 🚀 email: [email protected]

I’m excited to share a new demo video where I walk through how I built a complete Retrieval-Augmented Generation (RAG) workflow using n8n, OpenAI, and Pinecone — all without writing a backend or managing servers. This workflow turns unstructured documents into intelligent, answer-ready knowledge using automation and AI. 🔍 What the Demo Covers 📂 Pulling documents directly from Google Drive ✂️ Splitting text using a Recursive Character Text Splitter 📄 Loading & preparing data with a Data Loader 🧠 Generating embeddings with OpenAI 📦 Storing & indexing vectors in Pinecone 🤖 Using an AI Agent connected to the vector store 💬 Answering questions with accurate, RAG-powered context 🧾 Adding memory for more natural, human-like conversations 🎯 Why This Workflow Is Powerful This setup enables you to build: 🤖 AI chatbots with custom knowledge ❓ Automated Q&A assistants 🏢 Internal knowledge search tools 📚 Document-driven AI applications All created inside n8n — visually, modularly, and with full flexibility. 📽️ Watch the Demo I’ve recorded a full walkthrough to show how everything fits together from start to finish. 👉 Video attached If you're exploring RAG, vector databases, or AI automation, feel free to connect — always happy to share ideas and learn from the community! #n8n #AI #RAG #OpenAI #Pinecone #Automation #NoCode #LowCode #VectorDatabase #LLM #ArtificialIntelligence #WorkflowAutomation

In today’s data-driven world, achieving precise and contextually relevant search results is crucial. The RAG Hybrid Search, combining Keyword Search with Vector Search, offers a powerful solution to enhance search capabilities. 🔍 What is Hybrid Search? Hybrid Search blends the best of both worlds: 💡Keyword Search: Retrieves results based on exact term matches, ensuring precision. 💡Vector Search: Leverages deep learning models to capture semantic meaning, improving the understanding of user intent. 🎯 The Impact of Hybrid Search: By merging these two methods, Hybrid Search delivers smarter, context-aware results that improve user experiences and elevate search accuracy. This is especially valuable in handling unstructured data, making it ideal for applications such as recommendation engines, AI-driven search systems, and information retrieval. 💡 The Future of Search: With Hybrid Search, organizations can significantly enhance their data search capabilities, ensuring more relevant and intuitive results. As the landscape of AI and machine learning continues to evolve, leveraging these techniques is essential for staying ahead in the competitive market. hashtag#RAGHybridSearch hashtag#VectorSearch hashtag#KeywordSearch hashtag#AdvanceRag hashtag#AI hashtag#DataScience hashtag#SearchTechnology hashtag#Innovation hashtag#AIinSearch hashtag#InformationRetrieval

I'm excited to share a recent workflow I built that automates the process of collecting and managing contact information! Here's how it works: Form Submission 📝: Users fill out a "Contact Us" form with essential information—Name, Email, and Phone Number. Data Integration 🔗: After submission, the data is automatically added to a Google Sheet—updating or appending a new row with the user's details, submission time, and form mode. Instant Confirmation ✅: As soon as the data is stored, a Gmail email is sent to the user confirming their submission. Key Features of the Workflow: 1.Trigger: Form submission initiates the process 2.Data Handling: Google Sheets seamlessly stores and updates submissions 3.Communication: Instant email confirmation sent to users Benefits: 1.Streamlines contact management 2.Reduces manual data entry 3.Provides a professional and quick user experience