Human-in-the-loop approval for AI-assisted support deliverables

🚀 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

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Transparency isn't a vulnerability. It’s a competitive advantage in a market full of skeptics.

Let's be honest with one another in the comments.