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
Sector