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@article{202667,
        author = {Mohd Sabique Ansari},
        title = {Intelligent Customer Insight & Support Hub: A Unified NLP Framework},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {12647-12657},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202667},
        abstract = {Modern enterprises face an ever-growing challenge in processing vast volumes of unstructured customer feedback, support queries, and transactional data arriving continuously across multiple digital channels. This paper presents the Intelligent Customer Insight & Support Hub (ICISH), a unified Natural Language  
Processing (NLP) framework that integrates sentiment analysis, intent classification, named entity recognition (NER), and automated response generation into a single cohesive pipeline. The proposed framework leverages fine-tuned transformer-based architectures — specifically BERT and GPT-2 variants enhanced with a novel crosstask fusion mechanism — to deliver real-time customer insight extraction and intelligent support automation. Experimental results on three standard benchmark datasets demonstrate that ICISH achieves stateof-the-art performance across all NLP subtasks while maintaining inference latency below 100 milliseconds, meeting production-grade SLA requirements. Deployment in a partner enterprise environment yielded a 43% reduction in average customer resolution time and a 31% improvement in customer satisfaction scores compared to legacy rule-based systems. A knowledge distillation procedure compresses the full 12-layer encoder to a 6layer student model retaining 97.3% of task performance while delivering 46% lower latency.},
        keywords = {Natural Language Processing, Sentiment Analysis, Intent Classification, Transformer Models,   BERT, Customer Support Automation, Named Entity Recognition, Retrieval-Augmented Generation, Knowledge Distillation, Conversational AI},
        month = {May},
        }