RAG + AI Knowledge Automation
RAG Knowledge & Lead Management Assistant
A RAG-powered assistant that learns from business documents stored in Google Drive, answers questions using retrieved context, and connects lead intake to Asana and email notifications.
Knowledge Assistant
Grounded answers
Live activity
240
Documents
100%
Grounded
Auto
Leads
Section 01
The Problem
Knowledge is scattered
Policies, pricing and product details live across dozens of documents nobody can search quickly.
Answers take too long
Every question means opening files and hunting for the right paragraph by hand.
Leads handled manually
Incoming enquiries are copied between forms, task boards and inboxes one by one.
Disconnected systems
Knowledge, lead capture and follow-up all sit in separate tools with no shared flow.
Section 02
The Solution
Google Drive Ingestion
Business documents are pulled in and kept in sync automatically.
RAG-Powered Chatbot
A conversational assistant that answers from your own material.
Context-Based Answers
Retrieved passages ground every response instead of guesswork.
Lead Intake Form
Structured capture of new enquiries directly inside the flow.
Automatic Asana Tasks
Every lead becomes a task with the full context attached.
Email Notifications
The right person is alerted the moment a lead arrives.
Section 03
How It Works
Section 04
System Architecture
Google Drive
Source of every business document
n8n
Orchestrates ingestion, retrieval and routing
LLM / Embeddings
Turns documents into searchable meaning
Vector Knowledge Store
Retrievable chunks of company knowledge
RAG Assistant
Answers questions with retrieved context
Lead workflow branch
The lead form runs through the same n8n layer — creating an Asana task and firing an email notification so no enquiry sits unattended.
Section 05
Tech Stack
n8n
Workflow orchestration
Google Drive
Document source and sync
OpenAI / LLM
Reasoning and embeddings
RAG
Retrieval-augmented answering
Asana
Task and lead tracking
Email / APIs
Notifications and integrations
Section 06
What This Project Demonstrates
RAG Systems
Retrieval pipelines that keep answers grounded.
Document Automation
Turning static files into usable knowledge.
AI Assistants
Conversational interfaces built on real data.
Workflow Orchestration
Connected steps across multiple tools.
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