Success Case | GenAI Chatbot Boosts Customer Service Efficiency and Satisfaction with 60% Faster Response Times

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Multinational insurer of assistance to individuals and professionals, with a portfolio of +30M clients in 49 countries and a network of +35,000 employees.

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The Challenge

Insurer Company struggled with efficiently addressing customer inquiries about health insurance policy terms and conditions. Overwhelming documentation complexity and volume led to prolonged response times, impacting customer satisfaction.

The Solution

Keepler developed a sophisticated GenAI solution to improve customer service efficiency. It utilizes advanced ML to retrieve policy information.

 

  • Generative AI Chatbot: High accuracy, powered by a generative language model.
  • Streamlit Interface: Seamless user interaction platform.
  • Meta-information and Conversation Logs: enable continuous improvement analysis to compare AI-generated responses with human input.
  • Anonymization and Security: Sensitive data handled securely and compliantly.
  • Scalable Cloud Infrastructure: Built on AWS to ensure robust performance with increased user interactions.

Business Impact

  • Reduced consultation times by 60%, significantly streamlining customer service operations.
  • Customer satisfaction scores improved markedly, especially in service quality evaluations in health insurance queries.
  • The reduction in time spent is estimated to save significant labor costs annually, contributing to a strong ROI.
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    Keepler is a full-stack analytics services company specialized in the design, construction, deployment and operation of advanced public cloud analytics custom-made solutions. We bring to the market the Data Product concept, which is a fully automated, public cloud services-based, tailored software that adds advanced analytics, data engineering, massive data processing, and monitoring features. In addition, we help our customers transition to using public cloud services securely and improve data governance to make the organization more data-centric.

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