RAG Assistant with Document Embedding for FAQs

Overview: Chat with your company docs — answers with citations back to the source.

🔍 Problem: Teams and customers ask the same questions over and over, and the answers are buried in Drive/Notion docs nobody wants to dig through. Support time gets eaten by repetitive look-ups.

💡 Solution: A RAG assistant that ingests your documents, embeds them into a vector database, and answers questions in Slack or WhatsApp — with citations. Unknown questions escalate to a human with context attached.

Workflow Architecture: • Ingestion — pulls Google Drive / Notion files. • Chunk + embed — splits and embeds content into a vector DB. • RAG answering — retrieves relevant chunks and answers in Slack/WhatsApp with source citations. • Human escalation — unknown questions route to a person with the context included.

🛠 Tools: n8n · OpenAI Embeddings · Vector DB · Google Drive / Notion · Slack / WhatsApp

⚙️ Results: Instant answers grounded in your real docs · citations build trust · repetitive questions handled automatically, with clean human hand-off for the rest.

🌟 Impact: Turns scattered documentation into a self-serve assistant — cutting repetitive support load for staff and customers alike.