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RAG approach on Azure with OpenAI

Use of ChatGPT to increase the efficiency of analysts in the telecommunications industry

Challenge

  • Difficulties in quickly extracting relevant information from ticket data

  • Delays in the processing time

  • Reduced customer satisfaction

  • Customer churn

  • Improve analysts' efficiency in processing and analysing customer enquiries

  • The solution is based on a RAG (Retrieval Augmented Generation) approach. This technology made it possible to efficiently extract relevant information from an extensive pool of ticket data and integrate it into the analysis processes

  • Use of Azure Kubernetes Service (AKS) for scalability, high availability and efficient resource utilisation

  • OpenAI Service (OAI) with AI Search (AIS): Use of OAI for advanced AI models (LLM) and AIS for fast and precise data queries

  • Web chat frontend in React.js for convenient use

Solution

Value

  • Optimized RAG Analysis on Azure: Integrating the RAG approach on the Azure platform in conjunction with OpenAI creates a powerful and scalable analytical environment.

  • Ticket processing time reduced by 50%: The use of ChatGPT enables a significant improvement in analyst efficiency, leading to a 50% reduction in ticket processing time.

  • 10x Acceleration of Information Retrieval: The combination of RAG and ChatGPT allows for accelerated data analysis, resulting in faster insights and decision-making.

Roles

Cloud Engineer, Data Scientist, Machine Learning Engineer, Project Manager

Tools

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Sector

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