AI Ticketing Tool

AI IT Support Agent An intelligent IT Helpdesk solution that leverages a dual-model AI architecture to automatically analyze, route, and resolve corporate IT issues

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AI IT Support Agent

Inspiration

Corporate IT helpdesks often struggle with repetitive tickets, delayed resolutions, duplicate issue reporting, and inconsistent documentation. Employees frequently submit vague problem descriptions such as "VPN not working" or "Unable to access application," forcing support teams to spend valuable time gathering context before troubleshooting.

We wanted to build an AI-powered IT support assistant that could understand user issues, automatically create professional tickets, detect duplicate incidents, and instantly recommend proven resolutions from historical knowledge. Our goal was to reduce resolution time, improve ticket quality, and help IT teams focus on complex problems instead of repetitive tasks.

What it does

AI IT Support Agent is an intelligent helpdesk platform that automates ticket analysis, routing, and resolution using a dual-model AI architecture.

Key capabilities include:

  • AI Ticket Rephrasing: Converts informal user complaints into structured IT tickets.
  • Entity Preservation: Retains important technical details such as URLs, domains, ports, application names, and server references.
  • Duplicate Ticket Detection: Identifies existing tickets with similar issues and prevents unnecessary duplicates.
  • Knowledge Base Search: Uses semantic search to find previously resolved incidents that match a user's problem.
  • Resolution Recommendation: Suggests proven fixes from historical tickets.
  • AI Knowledge Base Creation: Converts support engineers' resolution notes into clean, searchable KB articles.
  • Success-Based Ranking: Prioritizes resolutions based on historical effectiveness.

How we built it

Google Gemini

Gemini serves as the reasoning engine of the platform.

It is responsible for:

  • Understanding unstructured user complaints.
  • Rephrasing issues into professional ticket formats.
  • Preserving critical technical entities.
  • Generating concise and reusable knowledge base articles from agent resolution notes.

Xenova Transformers

We integrated Xenova's all-MiniLM-L6-v2 embedding model directly into the Node.js backend.

This enables:

  • Semantic similarity search.
  • Duplicate ticket detection.
  • Knowledge base retrieval.
  • Fast local inference without requiring API calls for every search request.

MongoDB

MongoDB acts as the platform's memory layer.

Collections include:

  • Users
  • Tickets
  • Resolution Knowledge Base
  • Resolution History

Each knowledge article stores both its content and vector embedding, enabling efficient semantic search.

Backend & APIs

The application was built using:

  • Node.js
  • Express.js
  • MongoDB
  • Google Gemini API
  • Xenova Transformers

The backend orchestrates ticket creation, embedding generation, AI processing, KB retrieval, and recommendation ranking.

Challenges we ran into

Balancing AI Rephrasing with Technical Accuracy

A major challenge was ensuring Gemini improved ticket quality without removing critical technical details. Over-generalization could make troubleshooting difficult.

Duplicate Detection Accuracy

Traditional keyword matching failed when users described the same issue differently. We solved this by implementing semantic embeddings that compare meaning instead of exact wording.

Building a Searchable Knowledge Base

Raw resolution notes often contain inconsistent language and formatting. We had to create an AI summarization workflow that transforms support notes into standardized KB articles.

Cost Optimization

Running large language models for every operation can become expensive. By using local embeddings through Xenova, we reduced API dependency while maintaining high search quality.

Accomplishments that we're proud of

  • Successfully implemented a dual-model AI architecture.
  • Built semantic duplicate detection using local embeddings.
  • Automated ticket standardization with entity preservation.
  • Created a self-improving knowledge base powered by historical resolutions.
  • Reduced reliance on expensive LLM calls through efficient local vector search.
  • Designed a scalable architecture suitable for enterprise IT support environments.

What we learned

Through this project, we learned that effective AI systems often combine multiple specialized models rather than relying on a single large model.

We gained experience in:

  • Retrieval-Augmented Generation (RAG)
  • Vector embeddings and semantic search
  • Prompt engineering
  • Knowledge management systems
  • AI-powered workflow automation
  • Enterprise helpdesk processes

We also discovered the importance of balancing AI intelligence with practical operational requirements such as speed, cost, and reliability.

What's next for AI Ticketing Tool

We plan to expand the platform with:

  • Automated ticket routing to specialized support teams.
  • Multi-channel support via Email, Teams, Slack, and WhatsApp.
  • Predictive incident analysis to identify recurring problems.
  • Real-time AI chat support for instant troubleshooting.
  • Integration with ServiceNow, Jira Service Management, and Zendesk.
  • Resolution success analytics and continuous learning.
  • Voice-based ticket creation and support assistance.
  • Agentic AI workflows capable of executing approved remediation steps automatically.

Our long-term vision is to create a fully autonomous AI-powered IT support ecosystem that can resolve a significant percentage of enterprise IT issues without human intervention.

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