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CASE STUDY 01

Basic RAG Support System

THE PROBLEM

Generic AI chat answers sound confident but aren't grounded in a business's actual documentation, leading to inaccurate replies and repeated manual lookups.

THE SOLUTION

A self-updating RAG pipeline in n8n that syncs Google Drive changes into a Supabase vector store, so every AI response is grounded in current, real documentation.

TOOLS USED

n8n

Google Drive

Supabase

Vertex Embeddings

Gemini

Gmail

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CASE STUDY 01 — HOW IT WORKS

Basic RAG Support System

1

Trigger

Google Drive file created / updated / deleted events keep the knowledge base in sync automatically.

2

Embedding

Documents are processed and embedded with Google Vertex embeddings, then stored in Supabase Vector Store.

3

Retrieval + Generation

An AI Agent powered by Google Gemini pulls relevant chunks from the vector store and generates a grounded response.

4

Delivery

Updated handbook or document changes are automatically emailed to the relevant team via Gmail.

OUTCOME

A self-updating knowledge base that removes manual retraining or re-uploading — answers stay accurate because they are sourced from real content, not guessed.

Grounding AI support answers in real company documents

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