Project Title: Engineering an Automated

Project Title: Engineering an Automated Intelligence Pipeline (RAG Workflow)

The Challenge: In a world drowning in data, the problem isn't having information; it's accessing it instantly. Most businesses leave their most valuable assets buried in Google Drive folders where they remain static and underutilized. As a CS student and designer, I saw an opportunity to bridge the gap between "stored data" and "active intelligence." I wanted to build a system that doesn't just store files but understands them.

The Solution: I engineered a sophisticated Retrieval-Augmented Generation (RAG) pipeline using n8n for orchestration. This isn't just a simple link-up; it’s a multi-layered architectural approach to data automation:

Data Ingestion: Automatically triggering on Google Drive updates to ensure the AI's "brain" is always current.

Vector Processing: Implementing a Recursive Character Text Splitter to ensure data chunks maintain semantic meaning.

Memory & Storage: Utilizing Pinecone Vector Store for high-speed similarity searches, allowing for near-instant retrieval of relevant context.

Model Integration: Leveraging the power of Google Gemini as the reasoning engine, supported by OpenAI Embeddings for high-precision vectorization.

The Intersection of Design & Code: Being a 4th-semester BSCS student, I focused heavily on the logic of the workflow—ensuring the recursive splitting didn't lose context and that the vector database was optimized for performance. However, my background as a Graphic Designer allows me to visualize these complex backend processes into a user experience that feels intuitive. I believe that an automation is only as good as its usability; if the interface is clunky, the power of the AI is lost.

Why This Matters for Your Business: This workflow effectively creates a "Custom Brain" for your organization. Imagine chatting with your company’s entire history, SOPs, and technical documents as if you were talking to an expert teammate. By automating this pipeline, I eliminate the need for manual data entry or tedious searching, allowing teams to focus on high-level creative work while the AI handles the information retrieval.

Technical Stack Used:

Logic: n8n Workflow Automation

LLMs: Google Gemini & OpenAI

Database: Pinecone (Vector Search)

Storage: Google Drive API

Theory: RAG (Retrieval-Augmented Generation) & Semantic Search