Overview
An AI-powered customer support assistant that helps customers find pharmaceutical product information through natural-language conversations. It uses Retrieval-Augmented Generation (RAG) to retrieve relevant information from a centralised knowledge base and generate context-aware responses.
The problem
Pharmaceutical companies handle a lot of product information, from active ingredients and formulations to product details and uses. Finding the right information to answer customer questions can involve searching through multiple documents, making the support process slower and more demanding.
I wanted to explore how AI could make this information easier to access without requiring someone to manually search for every answer.
The solution
I developed a conversational assistant that retrieves relevant information from a pharmaceutical knowledge base before generating a response.
Instead of relying on the AI model alone, the system uses Retrieval-Augmented Generation (RAG) to find relevant information and provide it as context for the answer. This helps keep responses grounded in the available product documentation.
Tools used
n8n: Workflow automation and integration
Google Gemini: AI-powered response generation
Pinecone: Vector search and information retrieval
Google Drive: Pharmaceutical knowledge documents
APIs and JSON: Communication and data exchange
RAG: Connecting retrieved information to AI-generated responses
Walkthrough
A customer asks a question
The customer submits a question about a pharmaceutical product through the assistant’s conversational interface.
The workflow processes the question
Describe how the data is cleaned, checked and combined with information from other tools.
Relevant information is retrieved
Pinecone searches the indexed pharmaceutical knowledge base for information related to the customer’s question.
The answer is returned to the customer
The assistant presents the response through the conversational interface, making the information easier to access.
Results
The project demonstrates how AI and information retrieval can work together to make pharmaceutical product information more accessible.
The system is designed to reduce repetitive manual searches and support faster responses to common product questions.
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