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This capability utilizes natural language processing (NLP) to understand and respond to customer inquiries in real-time. It employs a transformer-based model trained on diverse customer service dialogues, allowing it to generate contextually relevant responses. The architecture supports multi-turn conversations, maintaining context across interactions to enhance user experience significantly.

This capability analyzes customer interactions to identify potential sales leads based on predefined criteria. It uses machine learning algorithms to score interactions and prioritize leads, integrating seamlessly with CRM systems to automate the follow-up process. The architecture allows for real-time updates and feedback loops to refine lead scoring models continuously.

This capability automates the process of sending follow-up messages to customers based on their previous interactions. It leverages a rule-based engine combined with machine learning to determine the optimal timing and content of follow-ups, ensuring that messages are personalized and relevant. The system can integrate with email and messaging platforms for seamless communication.