AI-Powered Customer Support Agent

Inspiration

I wanted to build something that solves a real problem using AI. Customer support teams deal with many messages every day, and answering similar questions repeatedly takes time. This inspired me to create a support agent that can understand customer queries, find similar past conversations, and suggest replies.

What it does

The project takes a customer message and predicts what type of issue it describes. It searches for similar conversations from the dataset and uses them as context for drafting a response. It can also flag unclear or potentially risky cases for human review.

How we built it

I used Python and Scikit-learn to build the main pipeline. I trained a Logistic Regression model using TF-IDF features to identify customer issues across 14 categories. I also added a similarity-based retrieval system to find related conversations, a reply-generation module, and rules to handle uncertain cases. Finally, I created evaluation scripts to test the model and compare it with a simple baseline.

Challenges we ran into

The biggest challenge was working with short customer messages that sometimes lacked enough information to identify the exact issue. Some categories also had similar wording, which affected classification. Another challenge was deciding when the system should suggest a reply and when it should ask for human help. I addressed this by adding confidence checks and escalation rules.

Accomplishments that we're proud of

I built a working prototype that combines intent classification, conversation retrieval, reply drafting, and escalation in one workflow. The classifier achieved 43.42% accuracy in five-fold cross-validation, compared with 11.32% for the majority-class baseline. I also created scripts to evaluate the system and documented the areas that need further improvement.

What we learned

This project helped me understand how NLP techniques can be used to solve practical problems. I gained experience with text preprocessing, machine learning, similarity search, and model evaluation. I also learned that an AI system should not always try to answer; it needs a way to recognize uncertainty and involve a human when necessary.

What's next for AI-Powered Customer Support Agent

I plan to improve classification accuracy, review the evaluation data with human annotators, and test different models. I also want to evaluate the quality of generated replies more thoroughly and improve how the system uses retrieved conversations. In the future, I would like to build a simple interface where support agents can review suggested replies and decide whether to send them or escalate the case.

Built With

• ai
• classification
• git
• github
• information
• llm
• logistic
• machine-learning
• natural-language-processing
• numpy
• pandas
• python
• regression
• retrival
• scikit-learn
• tf-idf
• vscode