NLP development for a pharmaceutical company
Challenge: The client, a multinational pharmaceutical company wanted to refactor an existing system for enquiry management and improve its performance and analytical capabilities through AI. The goal was to ensure a consistent customer experience across multiple locations.
Approach: Our team used semantic analysis with NLTK preprocessing to create different feature extraction and drug product labelling over already accumulated and classified еnquiry data. Preprocessed data were used to train Keras with the TF engine, and evaluated versus built-in SVM algorithms in SKLearn. Results were accuracy near 80% for classification. ML components were integrated into the auto-assign pipeline based on product and team recognized in free text еnquiry.
From a business perspective, the complete solution speeds up the process of enquiry management and contributes to overall customer satisfaction and increased operational efficiency.
Tech stack: Python and Angular developers, Data Scientists, ML Engineers, Project Manager.
Results: Successful completion of refactoring tasks and introduction of the NLP module. The achieved results were near 80% accuracy for the classification of enquiries.