The problem TicketIQ — Intelligent Support Triage System solves

Intelligent Customer Support Starts with Intelligent Ticket Triage

Modern businesses receive thousands of customer support requests every day through emails, chat platforms, and help desks. Manually reviewing every ticket, identifying its category, assigning priority, and routing it to the appropriate team is time-consuming and often leads to delayed responses.

TicketIQ automates this entire workflow using Machine Learning and Natural Language Processing (NLP). The system automatically classifies customer complaints, predicts their priority level, and suggests appropriate responses in real time.

By reducing manual effort and improving ticket routing accuracy, TicketIQ helps organizations respond faster, improve customer satisfaction, and increase operational efficiency.

Challenges I ran into

Challenges I Ran Into

Building TicketIQ involved several technical challenges beyond simply training a machine learning model.

One of the biggest challenges was working with unstructured customer complaints. Users often describe the same issue using different words, writing styles, abbreviations, and sentence structures. This required extensive text preprocessing, including cleaning, tokenization, stop-word removal, and TF-IDF vectorization to generate meaningful features.

Another challenge was accurately predicting ticket priority. While machine learning models performed well, certain urgent tickets containing critical keywords needed immediate attention. To improve reliability, I designed a hybrid approach that combines machine learning predictions with rule-based keyword detection for high-priority cases.

I also experimented with multiple algorithms—including Logistic Regression, Random Forest, and Naive Bayes—to identify the best balance between accuracy, speed, and generalization. After extensive evaluation, Logistic Regression achieved the highest performance with 86.68% category classification accuracy and 95.82% priority prediction accuracy, making it the production model.

Finally, deploying the application required optimizing model loading, integrating the trained models into a Flask backend, and ensuring seamless communication between the frontend, REST API, and prediction pipeline. Overcoming these challenges resulted in a fast, reliable, and user-friendly AI-powered support ticket triage system.