Hitesh Acharya 01 01 Behavioural pattern recognition in enterprise software platforms represents a critical capability for product analytics user experience optimisation fraud detection and customer health monitoring This paper presents a hybrid deep learning architecture that combines Bidirectional Encoder Representations from Transformers BERT for contextual feature extraction Convolutional Neural Networks CNN for local pattern detection and Bidirectional Gated Recurrent Units BiGRU for temporal sequence modelling The proposed architecture addresses the fundamental challenge that enterprise user behaviour is simultaneously contextual locally patterned and temporally dependent We evaluate the hybrid model on three enterprise use cases user churn prediction feature adoption forecasting and anomalous behaviour detection Experimental results on production datasets from a SaaS platform serving over 200 enterprise clients demonstrate that the hybrid architecture achieves a 73 improvement in F1score over the best singlearchitecture baseline and a 41 improvement over existing ensemble methods We also present a production deployment framework addressing latency constraints model serving infrastructure and continuous learning pipelines for enterprisegrade behavioural analytics Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026