Intelligent ticket routing, triage, and escalation preparation

I built an AI-powered support workflow that automatically triages incoming Zendesk tickets, drafts replies, and routes everything through a human approval layer before anything reaches the customer.

The goal wasn't to automate support entirely. It was to shift support agents from writers to editors: and that's a fundamentally different operating model.

Here's what the system does:
- Zendesk trigger fires on every new ticket or customer reply
- n8n fetches the thread and sends it to GPT-4o for analysis
- GPT-4o returns customer intent, risk level, confidence score, and a full draft reply
- Everything lands in a custom Control Room I built for human review
- Support agent approves  reply publishes directly to Zendesk in real time

Nothing reaches the customer without an explicit human decision.

If you'd like a deeper walkthrough of the tech stack, n8n workflow architecture, and what I'd improve, drop a comment or DM me and I'll share the extended version.

🚀 Built an AI-Powered Support Ticket Resolution Agent (RAG + Azure OpenAI + FastAPI + Next.js)
I’m excited to share a project I recently built — a fully functional AI-assisted Support Operations System designed to help teams move from raw customer tickets to approved responses in a structured workflow.
🎥 Demo video included below.
🔥 What it does:
✔ Create & manage support tickets
✔ Retrieve relevant knowledge base content (RAG with vector search)
✔ AI-powered classification (category, sentiment, priority)
✔ Generate response drafts using Azure OpenAI
✔ Human review + version control
✔ Approve & resolve workflow
✔ Zendesk webhook ingestion support

🧠 Tech Stack:
Frontend: Next.js + React + TypeScript
Backend: FastAPI + SQLAlchemy
Database: SQLite (local MVP)
Vector Store: Chroma
AI: Azure OpenAI (chat + embeddings)
Architecture: Modular service-based orchestration

💡 Why I built this:
Support teams often:
Spend too much time drafting repetitive replies
Struggle with knowledge base lookup
Lack structured AI review workflows
Need audit-friendly human approval
This agent bridges that gap with a controlled AI + human-in-the-loop workflow.

🏗 Architecture Highlights:
Ticket lifecycle tracking (NEW → APPROVED → RESOLVED)
RAG-based context retrieval
Suggestion versioning & review states
KB ingestion + reindexing
Webhook ingestion endpoint for Zendesk

🎯 Future Improvements:
Multi-tenant org support
Automated response dispatch
Advanced evaluation metrics for AI quality
Analytics dashboard for support KPIs

Would love feedback from:
Support Engineers
AI Engineers
SaaS founders
Anyone building AI-native internal tools

#AI #AzureOpenAI #RAG #FastAPI #NextJS #LLM #MachineLearning #CustomerSupport #GenAI #SupportAutomation #AIEngineering #StartupBuild

Customer support shouldn't be a pain point.

Long waits, frustrated customers, burned-out agents.

That's the status quo.

AI changes the game.

It's not about replacing support teams.

It's about giving them a weapon.

Automate FAQs, order statuses, simple issues.

Let your people focus on the tough stuff.

Smart routing gets requests where they need to go, fast.

Automated updates keep customers in the loop.

I've seen businesses cut costs by 30% and boost satisfaction at the same time.

That's not a pipe dream. That's AI working.

If you're still doing support manually, you're leaving money on the table.

It's time to automate, accelerate, and level up.

Because customer support isn't a cost. It's an asset.

And AI makes it unstoppable.

I've worked in MSPs for years and kept seeing the same issue: L1 escalations often lack useful troubleshooting context, forcing L2 to spend 10–15 minutes just reconstructing what’s going on. I built an AI-powered triage workflow where you paste raw ticket text and it outputs a consistent, structured “escalation-ready” package: ticket analysis (client/users/devices/environment), issue classification, missing information to collect, a diagnostic summary, recommended next steps, and an escalation note. The post shows three examples (M365 shared mailbox access, VoIP ring group routing, and EHR/iPad performance degradation) to demonstrate the standardized output and how it shines on “thin” tickets.