AI Customer Support Ticket Triage System
Budget: ₹600 – ₹1,500 INR
The project focuses on developing an AI-based Customer Support Ticket Auto-Triage System to automate the classification of customer queries. In modern applications, handling large volumes of support tickets manually is time-consuming and inefficient. This system aims to reduce manual effort by automatically categorizing incoming tickets into predefined categories such as Technical Issue, Billing Inquiry, Feature Request, Bug Report, and Account Management.
The system uses Natural Language Processing (NLP) techniques to process textual data from ticket subjects and descriptions. Initially, a TF-IDF based feature extraction method combined with Logistic Regression was implemented as a baseline model. Additionally, word embedding techniques are being explored to improve semantic understanding of the text.
To simulate real-world scenarios, two types of datasets were used: a structured synthetic dataset and a more realistic noisy dataset containing typos, mixed intent queries, and varying sentence structures. This helps evaluate the robustness of the model in practical situations.
The trained model is deployed using FastAPI, allowing real-time prediction through API endpoints. The system is capable of classifying tickets efficiently with low latency, making it suitable for integration into real-world customer support systems.
The system uses Natural Language Processing (NLP) techniques to process textual data from ticket subjects and descriptions. Initially, a TF-IDF based feature extraction method combined with Logistic Regression was implemented as a baseline model. Additionally, word embedding techniques are being explored to improve semantic understanding of the text.
To simulate real-world scenarios, two types of datasets were used: a structured synthetic dataset and a more realistic noisy dataset containing typos, mixed intent queries, and varying sentence structures. This helps evaluate the robustness of the model in practical situations.
The trained model is deployed using FastAPI, allowing real-time prediction through API endpoints. The system is capable of classifying tickets efficiently with low latency, making it suitable for integration into real-world customer support systems.