Customer Support

RAG-powered knowledge retrieval for ticket resolution

Use retrieval-augmented generation (vector search over a knowledge base) to pull relevant documentation and context for each ticket to reduce time spent searching.

Why the human is still essential here

Agents curate and maintain the knowledge base and decide which retrieved sources are trustworthy and applicable to the customer’s situation.

How people use this

Intercom Fin grounded knowledge retrieval

AI searches connected help-center sources and surfaces the most relevant articles/snippets to answer a customer question in context.

Intercom Fin AI Agent

Coveo AI search for agent assist

AI-powered relevance search retrieves the best KB content across systems using the ticket’s text as context to support faster resolution.

Coveo

Guru AI internal knowledge lookup

Agents query an AI knowledge base to retrieve verified internal documentation and troubleshooting steps while working a ticket.

Guru

Community stories (1)

LinkedIn

🚀 Built an AI-Powered Support Ticket Resolution Agent (RAG + Azure OpenAI + FastAPI + Next.js)

🚀 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

DV
Deepak VemulaAI Engineer at Quadrant Technologies
Feb 26, 2026