To design and develop a real-time Conversational AI system capable of understanding, processing, and responding to customer queries using advanced machine learning and Natural Language Processing (NLP) techniques.
To implement robust intent detection and sentiment analysis models for accurate interpretation of customer messages and contextual understanding of user requirements.
To train deep learning-based neural network architectures using historical customer interaction data to improve response relevance, accuracy, and efficiency.
To optimize the conversational AI system for scalability, low latency, and high-performance deployment in real-time customer support environments.
To evaluate system effectiveness using ke
Collect, curate, and preprocess large-scale customer support datasets including chat transcripts, emails, and historical support ticket interactions.
Perform data cleaning, normalization, tokenization, and labeling for intent classification and sentiment analysis model training.
Design and implement a neural network-based Conversational AI architecture using deep learning frameworks such as TensorFlow or PyTorch.
Develop and integrate Natural Language Understanding (NLU) components for intent detection, entity recognition, and sentiment analysis.
Train and fine-tune machine learning models using supervised learning techniques on labeled customer interaction datasets.
Optimize the system for real-time performance by improving inference speed, reducing latency, and ensuring scalable deployment architecture.
Conduct experimental evaluations using metrics such as precision, recall, F1-score, response time, and customer satisfaction ratings.
Continuously improve the system through iterative testing, error analysis, and model retraining based on live feedback and interaction logs.
