🚀 AI-Powered Support Ticket Classifier Automatically classify customer support tickets by issue type and urgency, and extract product names, complaint keywords, and dates — with an interactive Gradio UI.

Ai-Ticket-Classifier is a machine learning and NLP-based system that automates the classification of customer support tickets. It preprocesses ticket data, trains models, and categorizes incoming tickets into predefined categories, helping streamline ticket triaging and improve response efficiency. The repo includes a web interface, reporting tools

This is a project to classify customer requests based on the customer request text using AI - NLP. I have used AWS Comprehend, AWS Step Functions and Lambda Functions. In addition to these components, Zendesk supports natively connection to the AWS Event Bridge, this means that all ticket events in Zendesk are sent to AWS to process and update the ticket. You can consider and Event Based Architecture for your AI project as well.

AIML Online Capstone AUTOMATIC TICKET ASSIGNMENT The Real Problem One of the key activities of any IT function is to “Keep the lights on” to ensure there is noimpact to the Business operations. IT leverages Incident Management process to achieve theabove Objective. An incident is something that is unplanned interruption to an IT service orreduction in the quality of an IT service that affects the Users and theBusiness. The main goalof Incident Management process is to provide a quick fix / workarounds or solutions thatresolves the interruption and restores the service to its full capacity to ensure no businessimpact.In most of the organizations, incidents are created by various Business and IT Users, End Users/ Vendors if they have access to ticketing systems, and from the integrated monitoringsystems and tools. Assigning the incidents to the appropriate person or unit in the support team has critical importance to provide improved user satisfaction while ensuring better allocation of support resources. The assignment of incidents to appropriate IT groups is still a manual process in many of the IT organizations.Manual assignment of incidents is time consuming and requires human efforts. There may bemistakes due to human errors and resource consumption is carried out ineffectively because ofthe misaddressing. On the other hand, manual assignment increases the response and resolution times which result in user satisfaction deterioration / poor customer service. Business Domain Value In the support process, incoming incidents are analyzed and assessed by organization’s support teams to fulfill the request. In many organizations, better allocation and effective usage of the valuable support resources will directly result in substantial cost savings. Currently the incidents are created by various stakeholders (Business Users, IT Users and Monitoring Tools) within IT Service Management Tool and are assigned to Service Desk teams (L1 / L2 teams). This team will review the incidents for right ticket categorization, priorities and then carry out initial diagnosis to see if they can resolve. Around ~54% of the incidents are resolved by L1 / L2 teams. Incase L1 / L2 is unable to resolve, they will then escalate / assign the tickets to Functional teams from Applications and Infrastructure (L3 teams). Some portions of incidents are directly assigned to L3 teams by either Monitoring tools or Callers / Requestors. L3 teams will carry out detailed diagnosis and resolve the incidents. Around ~56% of incidents are resolved by Functional / L3 teams. Incase if vendor support is needed, they will reach out for their support towards incident closure. L1 / L2 needs to spend time reviewing Standard Operating Procedures (SOPs) before assigning to Functional teams (Minimum ~25-30% of incidents needs to be reviewed for SOPs before ticket assignment). 15 min is being spent for SOP review for each incident. Minimum of ~1 FTE effort needed only for incident assignment to L3 teams.During the process of incident assignments by L1 / L2 teams to functional groups, there were multiple instances of incidents getting assigned to wrong functional groups. Around ~25% of Incidents are wrongly assigned to functional teams. Additional effort needed for Functional teams to re-assign to right functional groups. During this process, some of the incidents are in queue and not addressed timely resulting in poor customer service.Guided by powerful AI techniques that can classify incidents to right functional groups can help organizations to reduce the resolving time of the issue and can focus on more productive tasks.

Ai-Ticket-Classifier-3.0 is a machine learning and NLP-based system that automates the classification of customer support tickets. It preprocess ticket data, trains models, and categorizes incoming tickets into predefined categories, and shows real time recommended solutions. Also have Content Gap Analysis feature.This tool also have GUI .

Ai-Ticket-Classifier-2.0 is a machine learning and NLP-based system that automates the classification of customer support tickets. It preprocesses ticket data, trains models, and categorizes incoming tickets into predefined categories, and shows real time recommended solutions. Also have Content Gap Analysis feature.This tool also have GUI .

Ai-Ticket-Classifier-2.0 is a machine learning and NLP-based system that automates the classification of customer support tickets. It preprocesses ticket data, trains models, and categorizes incoming tickets into predefined categories, and shows real time recommended solutions. Also have Content Gap Analysis feature.This tool also have GUI .

Team-built AI chatbot and ticketing system using Flask, OpenAI API, and Python to automate customer support workflows. Handles real-time queries, classifies issues, and auto-generates tickets aligned with ITIL practices. Scalable backend, multilingual-ready, and designed for global support teams.

End-to-end AI helpdesk platform where support tickets are triaged by an intelligent agent — classifying, retrieving knowledge base articles, drafting replies, and managing resolution with audit logs and role-based access.

Bulk classify support tickets with AI. BYOK.

AI-powered banking support ticket classifier that determines whether tickets need AI code remediation or Vibe-coded troubleshooting scripts

Gemini-powered AI that classifies support tickets and provides quick, accurate responses.

AI-powered system for classifying IT support tickets, predicting priority, and analyzing customer issues.

A sophisticated AI-powered support ticket resolution system built with LangGraph that automatically classifies, processes, and responds to support tickets with intelligent escalation handling.

An AI-powered customer support system that automatically classifies tickets and provides intelligent responses using Retrieval-Augmented Generation

I built TriageAI — an AI system that automatically classifies and routes customer support tickets using LangChain and Grok.

AI-powered NLP API that classifies, prioritizes, and extracts insights from support tickets using FastAPI and ML models.

AI-powered tool that classifies, prioritizes, and generates replies for support tickets using OpenAI. Built to showcase automation skills.

AI powered Gmail support agent that classifies, replies and escalates emails with JIRA tickets using LangGraph, Groq and Gmail API.

🤖 Classify support tickets and generate automated responses with this AI-powered bot using a BERT model for efficient customer service.

AutoTicket Triage & Resolution Agent — An Agentic AI system built with LangGraph and FastAPI that automatically classifies, prioritizes, and resolves support tickets using open-source LLMs, Redis, and SQLite.

TicketFlowAI is a web application that helps manage tech support tickets to streamline an IT department helpdesk environment. It is comprised of two parts, and this is the front end. TicketFlowAI connects to AWS AI services such as Comprehend and Bedrock to classify ticket priority, complexity, and the need for human interaction according.

End-to-end AI help-desk automation to replace manual ticket triage for an IT Support Team. Classifies support emails by complexity, performs RAG lookup for incident resolution, and routes incidents accordingly. Utilizes n8n, Postgres, Pinecone, and OpenAI LLM models powering classification, summarization, and reasoning, all integrated through APIs.

DeepCSAT is an AI-based system that predicts customer satisfaction (CSAT) from e-commerce support tickets using NLP and a deep neural network. It analyzes customer remarks, product category, sentiment scores, and item price to classify whether a customer is Happy or Unhappy, helping businesses quickly understand customer feedback.

AI Support Ticket Classifier

Trouble Ticket Support

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A Streamlit app where users paste a customer complaint, and it auto-tags the issue type (e.g. "Network", "Billing", "Hardware") using a generative model.

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