I wanted to share something I’ve been working on: *RAG-Guard*, a document AI that’s all about privacy. It’s an experiment in combining Retrieval-Augmented Generation (RAG) with AI-powered question answering, but with a twist — your data stays yours.

Here’s the idea: you can upload contracts, research papers, personal notes, or any other documents, and RAG-Guard processes everything locally in your browser. Nothing leaves your device unless you explicitly approve it.

### How It Works

- * Zero-Trust by Design*: Every step happens in your browser until you say otherwise.

- * Local Document Processing*: Files are parsed entirely on your device.

- * Local Embeddings*: We use [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v...) via Transformers.js to generate embeddings right in your browser.

- * Secure Storage*: Documents and embeddings are stored in your browser’s encrypted IndexedDB.

- * Client-Side Search*: Vector similarity search happens locally, so you can find relevant chunks without sending anything to a server.

- * Manual Approval*: Before anything is sent to an AI model, you get to review and approve the exact chunks of text.

- * AI Calls*: Only the text you approve is sent to the language model (e.g., Ollama).

No tracking. No analytics. No “training on your data.”

### Why I Built This

I’ve been fascinated by the potential of RAG and AI-powered question answering, but I’ve always been uneasy about the privacy trade-offs. Most tools out there require you to upload sensitive documents to the cloud, where you lose control over what happens to your data.

With RAG-Guard, I wanted to see if it was possible to build something useful without compromising privacy. The goal was to create a tool that respects your data and puts you in control.

### Who It’s For

If you’re someone who works with sensitive documents — contracts, research, personal notes — and you want the power of AI without the risk of unauthorized access or misuse, this might be for you.

### What’s Next

This is still an experiment, and I’d love to hear your thoughts. Is this something you’d use? What features would make it better?

You can check it out here: [https://mrorigo.github.io/rag-guard/]

Looking forward to your feedback!