Automated Ticket Sorting System
Project Overview
A global SaaS provider with over 50,000 monthly inbound support queries needed a smarter and faster way to manage ticket classification. Their support division spanned multiple regions, offered multilingual assistance, and dealt with a rising number of repetitive inquiries.
As the company scaled, the volume of support tickets grew faster than the team could handle, causing inefficiencies in ticket routing and response management.
Challenges & Constraints
The client’s existing workflow required agents to manually open, label, classify, and route each request. This resulted in:
- High response delays during peak traffic
- Inconsistent categorization due to human error
- Priority tickets being overlooked or mislabeled
- Increased operational cost because of growing manpower needs
- Limited visibility into ticket trends and root causes
They needed an ML-powered system that could intelligently categorize tickets in real time with high accuracy.
Project Solution
MindRind built a custom machine-learning ticket classification ecosystem, designed to analyze and classify support messages instantly.
Key components included:
- Automated Intent Detection
Identified the purpose of each message (refund request, login issue, product query, billing, etc.) - Urgency & Sentiment Analysis
Evaluated emotional tone and urgency level to prioritize critical cases. - Automatic Ticket Routing
Directed cases to the correct department based on classification rules. - Quality Control Dashboard
Enabled supervisors to review confidence scores, adjust rules, and retrain models.
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Client Satisfaction Rate
Our Approach
- Data Preparation
Cleaned and labeled 100,000+ historical support messages to build a reliable training dataset. - Model Architecture Design
Trained multiple NLP-based models and selected the highest-performing variant based on accuracy and recall. - System Integration
Integrated the ML engine into the client’s CRM and support tools to enable end-to-end automation. - Continuous Improvement Cycle
Implemented a feedback loop allowing agents to correct classifications, improving accuracy over time.
Technologies Used
Python • FastAPI • NLP Pipelines • Cloud ML Deployment
Results
- 72% reduction in manual ticket classification
- 40% faster first response time
- 91%+ model accuracy after fine-tuning
- 250–300 agent hours saved monthly
- Support agents could finally focus on complex and high-value tickets
Client Impact
The organization saw a significant increase in customer satisfaction and operational efficiency. Ticket routing became more predictable, workloads stabilized, and management gained visibility into emerging customer issues.
Let's Address Your Questions Today!
Yes, models can be trained using multilingual datasets for global support teams.
Agents can override it, and the system learns from corrections to improve accuracy.
It reduces workload, allowing teams to scale support efficiently without additional hiring.
Absolutely. The API-driven architecture supports integration with any CRM or ticketing system
Project Name
Automated Ticket Sorting System
Category
AI/ML
Duration
3 Months