AI Conversation & Q&A Service

What is RAG?

As the amount of information and documents grows across different sources such as websites, PDFs, and media, it becomes like an ocean that is difficult to navigate. With the rise of generative AI, users now have the ability to talk to information instead of searching for it manually.

Retrieval-Augmented Generation (RAG) is an AI approach that enables users to convert their information — whether text, websites, images, or documents — into an interactive chatbot. This allows users to communicate directly with their data.

RAG chatbot services extend beyond simple chat and include: document Q&A systems, conversational AI for business documents, and knowledge assistants trained on specific domains.

Use Case: Skincare RAG Application

This solution presents a RAG application designed to assist users based on skincare brochures and product documents. The dataset includes scientific names, chemical ingredients, directions for use, and skin conditions for each product.

The RAG system is trained on these documents and converted into a chatbot ready to answer queries such as "Which product should I buy based on my skin condition?" — and can escalate to a human specialist when needed.

Product Documents

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How the Pipeline Works

The pipeline consists of three core RAG stages: converting PDF documents into embeddings, indexing them in a vector database, and setting up the Q&A interaction. It monitors response quality with accuracy potentially reaching up to 90%, and can be deployed on WhatsApp, email, or web.

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Product Search Workflow