The AI comes to your data. Not the other way around.
If a stranger walked up to me on the street and asked to read my notes, my work documents, the questions I've been turning over in my head for the past six months — I'd say no. Obviously.
But every day I open an AI assistant and do exactly that.
I use AI for everything. Thinking through decisions, drafting, researching, remembering things I'd otherwise lose. It's genuinely useful and I'm not giving it up. But there's always this low-level discomfort in the background. A feeling that I'm sharing things I probably shouldn't, with a company I don't fully understand, under terms I didn't really read.
I'm a person who thrives on convenience. And for a long time, convenience was winning.
The deal is always the same. You want a smarter assistant, so you upload your notes. You connect your calendar. You paste in the document you've been wrestling with for weeks. And somewhere in the background, all of it lands on a server you don't control, indexed by a company whose business model you're not entirely sure about.
Nobody forces you. You click agree. But the trade is always the same: intelligence in exchange for access.
I didn't want to make that trade anymore.
So I built Lumogis.
It's a self-hosted, local-first AI platform you run yourself, on your own hardware, in your own home, under your own terms. Your documents, notes, and conversations stay on your machine. The indexes that make retrieval possible stay on your machine too. When you ask a question, Lumogis finds the relevant context locally, assembles it, and sends only that composed prompt to an LLM. Or, if you prefer, keeps inference fully local with Ollama.
Nothing gets bulk-uploaded. Nothing gets handed to a SaaS indexer. You can read every line of how it works, because it's AGPL-3.0 and the source is all there.
I'm not anti-cloud. I still use cloud models. I like convenience as much as the next person. That's exactly how I ended up here.
But I think there's a meaningful difference between choosing to send something out and having no other option. Most AI tooling today only offers the second. The moment you want memory, retrieval, context across sessions, you have to trust someone else with the raw material of your thinking.
That felt wrong to me. Lumogis is the answer I built for myself, and I'm making it available to anyone who feels the same way.
It's early. There's a lot still to build. But the foundation is solid: Docker Compose, Qdrant for vectors, Postgres for metadata, a FastAPI orchestrator, and a clean web UI. It works today for household use, one person or a whole family on a LAN, each with their own identity and memory, with an audit log on every action that matters.
If you've ever felt that low-level discomfort, if you've ever clicked agree while knowing you probably shouldn't, I'd love for you to take a look.
⭐ Star Lumogis on GitHub and follow along. We're just getting started.