High Inquiry Volumes and Slow Response Times Problem

Daniela Todorova

2 min read

Updated: Feb 24

What is Retrieval-augmented generation (RAG)?

RAG uses both retrieval and generative AI, ensuring that results are factually appropriate in human language that is contextual. It enhances results by pulling relevant, unique answers that feel more natural and precise, compared to generative AI alone, which does not take domain-specific data into consideration in the processing of queries.

How RAG Works

RAG pulls in relevant content from internal databases, like FAQs or past tickets, and injects it right into a generative AI model. This ensures accurate, current responses for specific queries and reduces the chance of misinformation that can be seen with generative AI operating independently.

Retrieval Augmented Generation minimizes hallucinations by supplying domain-specific data to the LLM via its context window, ensuring accurate and relevant outputs. https://www.pinecone.io/

Client Background

Our client is one of the leading Bulgarian online stores dealing in a wide portfolio of products: video games, books, electronic gadgets, and many others. With a growing customer base, effective support became crucial for maintaining customer satisfaction and retention.

Challenges

The support team faced an increased volume of inquiries requiring detailed and specific responses. This was highly time-consuming if done manually, impacting response times and overall efficiency.

Solution: RAG Implementation

RAG was implemented to process customer queries by searching internal databases, including past tickets and technical documents, for relevant information. It generates accurate responses and flags complex cases for human support.

Key Benefits

Increased support capacity through automation of first-level responses.
Faster response times, enhancing the customer experience.
Improved support during releases by leveraging updated technical documentation.
Higher customer ratings and retention, ensuring platform growth.

Outcomes

RAG implementation streamlined customer support by automating first-level responses with accurate, context-aware answers. This reduced response times, improved efficiency, and enhanced customer satisfaction, leading to higher retention and business growth.

Unleash the Power of Retrieval-augmented generation (RAG)

RAG technology modernizes businesses by automating complex support processes and improving customer satisfaction. Ready to optimize? Get in touch with us today for a personalized RAG solution tailored to your needs.