Automated Multi-Agent AI Support & Lead Triage Pipeline

Are your high-ticket clients waiting hours for an email response? This intelligent multi-agent n8n workflow instantly screens, analyzes, and responds to customer emails in real-time, utilizing advanced RAG (Retrieval-Augmented Generation) to deliver human-like support instantly.

Project Overview:

This is an enterprise-grade AI automation system designed to eliminate manual customer support queues. Instead of simple auto-replies, it uses a multi-agent routing structure combined with a dynamic knowledge base to handle complex inquiries autonomously.

How It Works (Under the Hood):

Instant Inbound Triage: A Gmail Trigger catches incoming emails instantly, extracting raw content for processing.

AI Intent Classification: An initial OpenAI model acts as a gatekeeper, analyzing the email to determine if it is a valid customer support request or irrelevant noise.

Conditional Routing: An advanced router splits the path: non-support emails receive a polite automated Telegram update, while actual support tickets are routed to the main AI engine.

Context-Aware AI Agent: The core Customer Support Agent is equipped with an OpenAI Chat Model, conversational memory, and a custom Vector Store Tool.

Pinecone RAG Integration: The agent queries a Pinecone Vector Database (powered by OpenAI Text Embeddings) to fetch real-time, accurate company documentation and context, eliminating hallucinations.

Automated Action & Response: Once the resolution is drafted, the system automatically creates a draft in Gmail for review and sends an instant internal notification via Telegram.

Why This Wins Clients (The Value Pitch):

Zero Hallucinations: Connected to a live vector database (Pinecone) so the AI only speaks from approved company data.

Reduced Overhead: Cuts down customer support response times from hours to under 60 seconds.

Production-Ready Architecture: Designed with modern n8n AI sub-nodes, structured tools, and modular scaling capabilities.

For a YouTube live streamer, there was a clear problem: viewers often caught amazing moments on stream but had no easy way to save or share them without scrubbing back through hours of footage. The creator wanted something lightweight that empowered the audience to create clips during the livestream itself, helping turn viewers into active contributors rather than passive watchers. The goal was a fast MVP that could plug directly into existing YouTube livestreams without forcing anyone to learn complex editing tools.

Using React.js and Supabase, a full‑stack web app was built at snipeme.xyz that lets live viewers capture specific moments from an ongoing stream and turn them into short clips. The frontend focuses on a clean, responsive experience so anyone in chat can quickly mark and trim a highlight while the stream is still live. Supabase handles authentication, clip metadata, and storage, keeping the flow snappy and reliable while making it easy to manage clips over time.

The result is an audience-driven clipping tool that helps streamers get more shareable content with almost no extra effort on their side. Viewers can instantly save hype plays, funny moments, or key reactions, which can then be reused across YouTube Shorts and other platforms to grow the channel. This project showcases the ability to design around community interaction, ship a focused MVP quickly, and use modern tools like React and Supabase to support real-time creator workflows.