How to Build a Private Knowledge Base on Your Phone With Local AI (No Cloud, No API Keys)

#softwaredevelopment #ai #privateai

Most RAG setups require a cloud database, an embedding API, and a hosted LLM. You upload your documents to someone else's server, pay per token to process them, and hope their privacy policy actually means something.

There is another way. You can build a knowledge base entirely on your phone, indexed locally, with AI inference running on your own hardware. No cloud. No API keys. No monthly bill.

Off Grid has built-in projects with RAG support. Attach your documents, ask questions, and the AI searches through your files and generates answers grounded in your actual data. The whole pipeline runs on hardware you own.

Off Grid connecting to local and network models - the same setup that powers the knowledge base feature.

How it works

Create a project

Open Off Grid. Create a new project. Give it a name - "Legal contracts," "Product research," "Codebase docs," whatever you are working on.

Add your documents

Attach files to the project. Off Grid supports PDFs, code files, CSVs, text files, and more. The documents are processed and indexed locally on your phone. Nothing is uploaded anywhere.

Ask questions

Start a conversation within the project. When you ask a question, Off Grid searches through your attached documents, finds the most relevant passages, and feeds them as context to the AI model. The model generates an answer grounded in your actual documents, not in its general training data.

This is RAG - retrieval-augmented generation - running entirely on your own devices.

Pick your model

Here is where it gets interesting. Off Grid lets you choose which model handles the inference, and you can switch mid-conversation.

On-device models: A 2B model running on your phone handles simple lookups and short summaries well. It works offline, on airplane mode, anywhere. Good for quick references.

Remote models: If you have Ollama or LM Studio running on your Mac or PC, Off Grid auto-discovers them on your network. Point your project at Qwen 3.5 9B running on your desktop, and you get dramatically better reasoning, synthesis, and analysis over your documents. The 262,000 token context window means the model can work with long documents and multiple sources at once.

You can start with the on-device model while commuting, switch to the remote 9B when you get home, and the project context stays the same.

What this is good for

Research. Attach a collection of papers or reports. Ask the AI to find connections between them, summarize key findings, or answer specific questions that span multiple documents. The model only draws from what you have given it, so you know where the answers come from.

Legal documents. Upload contracts, agreements, or compliance docs. Ask specific questions: "What are the termination clauses across these three contracts?" "Does this agreement have a non-compete?" The model searches through the text and gives you specific answers with the relevant passages.

Codebase understanding. Attach source files, READMEs, and architecture docs to a project. Ask "How does the authentication flow work?" or "What does this function do?" Useful when onboarding to a new codebase or reviewing unfamiliar code.

Personal notes. Attach your notes, journal entries, meeting summaries. Build a searchable, conversational interface over your own writing. "What did I decide about the pricing model in last week's notes?" The AI finds it and tells you.

Sensitive data. Medical records, financial documents, proprietary business information. Any data you would never want on someone else's server. With Off Grid, it never leaves your device. The knowledge base is local. The inference is local (or on your own network). The data trail ends at your WiFi router.

Why this matters

Every major RAG solution today requires sending your documents to a cloud service. Pinecone, Weaviate, Chroma hosted, OpenAI embeddings - they all involve uploading your data. For many use cases, that is fine. For sensitive data, it is a dealbreaker.

Off Grid's approach is different. The entire pipeline - document processing, embedding, retrieval, and inference - runs on hardware you control. Your phone handles the knowledge base. Your desktop (via Ollama or LM Studio) handles the heavy inference. Your WiFi connects them. That is the full stack.

You paid for this hardware. Your documents are your data. You should be able to build intelligence on top of both without asking permission from a cloud provider.

You do not need anything you do not already own

If you have a phone and documents, you can start today with on-device models. If you have a Mac or PC, add Ollama or LM Studio to get better model quality. Qwen 3.5 9B on a MacBook with 16GB RAM gives you a knowledge base assistant that rivals anything you would get from a cloud RAG setup.

No new hardware. No subscriptions. No API keys. Just your devices, your documents, and your questions.

Where Off Grid is heading

The knowledge base is one piece of a larger vision: a personal AI operating system that runs across every device you own. Network discovery, on-device inference, tool calling, vision, and voice are all in Off Grid today. The next steps are shared project context across devices and automatic model routing based on query complexity.

We are building this in the open. Join the Off Grid Slack from our GitHub.