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Building a system that identifies optimal moments for proactive customer outreach based on usage patterns, lifecycle stage, and predicted needs. Aims to transform support from reactive to proactive.

Implementing real-time sentiment analysis for phone support calls. The system provides live coaching prompts to agents based on detected customer emotions and conversation dynamics.

Developed an AI-powered onboarding assistant that guides new customers through product setup via natural conversation. The bot answers questions, provides personalized recommendations, and escalates to human agents when needed.

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ML system that predicts which support conversations are likely to escalate, allowing supervisors to intervene proactively. Analyzes conversation patterns, customer history, and real-time sentiment.

Built a machine learning model that predicts customer churn risk 30 days in advance. The model analyzes usage patterns, support interactions, billing history, and engagement metrics to identify at-risk customers before they leave.

We implemented an intelligent ticket routing system that uses NLP to analyze incoming support tickets and automatically route them to the most appropriate team. The system considers ticket content, customer history, and agent expertise to make routing decisions.

Deployed a support chatbot that handles inquiries in 12 languages with automatic language detection. Uses fine-tuned models for each language to ensure cultural nuance and accuracy.

Created a system that suggests relevant responses to support agents in real-time. Uses RAG (Retrieval Augmented Generation) to pull from our knowledge base and previous successful resolutions.