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AI Knowledge Base (RAG) Chatbot
Overview
Developed a Retrieval-Augmented Generation (RAG) chatbot that indexes documents uploaded to Google Drive, stores them in a vector database, and answers user questions using the company's own knowledge.
Business Value
- Gives employees instant access to company information
- Reduces time spent searching through documents
- Improves the accuracy of AI responses
- Makes internal knowledge easier to access
Technologies
n8n • Google Drive • Pinecone • Google Gemini Embeddings • OpenRouter • AI Agent
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Related posts
Processing incoming PDF attachments and logging key email data manually takes hours of tedious, repetitive work. Every time a new invoice, quote, or report lands in your inbox, you have to manually open the email, download the PDF, read through dense pages to extract important details, format the data, and log it into Google Sheets row by row.
Which one is faster, saves more time, and prevents mistakes?
1 voted
25%
3 voted
75%
4 votes
Closed
The manual-vs-automated framing is clear, but I'm curious how the workflow copes with PDFs that don't share a layout, like scanned invoices versus quotes. Do you parse them with a fixed template or an extraction step, and what happens to rows it can't read confidently?
Your business knowledge can now answer customers for you.
I build AI assistants that pull from your own information, so replies stay accurate and on-brand.
When something needs your attention, I set it up to alert you on Telegram straight away.
It’s a simple way to save time without losing control.
What would you want your assistant to handle first?
For an AI scribe, I'd test what happens when someone switches encounters while a draft is still generating.
In my Arogya / Aurora EHR work, typed or dictated rounds become editable drafts. AI content stays advisory, and a clinician explicitly approves anything added to the chart.
The first response should stay attached to its original encounter. Before saving, I'd check that the encounter being reviewed still matches the draft, as well as requiring clinician approval.
That context check belongs in the save workflow, even when the draft reads well.
kapilchauhan.netlify.app
Arogya / Aurora EHR: AI for inpatient care | Kapil Chauhan
Production hospital and EHR platform that accelerates rounds, documentation, records, and care operations with clinician-reviewed AI.