Enhancing Support and Content with Real-Time RAG

Challenge

Our client, a mid-sized enterprise in a highly competitive industry, struggled to maintain fast and accurate customer support responses and generate engaging marketing content. The challenge was to implement a system that could leverage real-time data to ensure up-to-date information, while also providing flexibility in model selection to meet various user needs.

Project Goals

1. Decrease customer support response times and improve the accuracy of responses using real-time data.
2. Automate the generation of high-quality, engaging marketing content.
3. Ensure the system is constantly updated with the latest information through real-time data integration.
4. Enable flexible model selection for different user requirements.

Our Solution

Beryllium Studios implemented a real-time Retrieval-Augmented Generation (RAG) system that leveraged the latest NLP advancements. The system was designed with the following key components:

1. Data Collection and Real-Time Updates: Integrated internal and external data sources with continuous real-time updates using a Redis vector database.
2. Model Selection and Pipeline Development: Utilized a combination of pre-trained transformer models for retrieval and fine-tuned LLMs (like Mistral and Llama) for generation. The pipeline was designed to allow multi-LLM selection based on specific user needs.
3. System Architecture and Integration: Built using FastAPI and LlamaIndex, hosted on AWS for scalability and reliability, ensuring efficient handling of API requests and real-time processing.
4. User Interface: Developed user-friendly interfaces for customer support agents and a dashboard for the marketing team to request and review generated content.

Results

1. Customer Support:
   • Response Time: Decreased by 40%, significantly improving support efficiency.
   • Customer Satisfaction: Increased by 25% due to accurate and timely responses.
2. Content Generation:
   • Productivity: Marketing team productivity increased by 50%.
   • Engagement: A 30% increase in social media interaction rates due to high-quality, timely content.
3. Flexibility and Customization:
   • The multi-LLM selection feature allowed tailored responses and content, meeting various user needs and improving overall user satisfaction.