Your Data, Your Rules: Inside Homedock OS

Article by Claudio González, CEO at Banshee Technologies, published in AI Horizon Journal for AI Ethics & Integrity International Association.

Banshee Technologies develops HomeDock OS, a cloud operating system that turns any computer into a private cloud server with over 200 self-hosted applications. HomeDock OS Enterprise brings local AI, end-to-end encryption, and EU-compliant data sharing to organizations that refuse to compromise on data sovereignty. 

How do you plan to achieve data privacy?

We built HomeDock OS around one principle, your data should stay yours. The platform follows an open-core model, anyone can use it on our managed cloud or install it offline on Windows, macOS, or Linux. Our Enterprise modules, Local GPT, Secure Tunnel, Data Space, are exclusively on premise. That’s intentional. If a bank needs AI that never phones home, or a government agency needs encryption beyond HTTPS, those tools should only run on hardware they physically control. No telemetry, no cloud dependency.

What is the concept of Local AI for digital services?

Local AI, for us, means running language models directly on your own servers using open-source, quantized models. No cloud subscriptions, no data leaving your network, ever. Our Local GPT module runs models ranging from lightweight 0.5B-parameter on modest hardware to advanced 20B-parameter reasoning models like OpenAI GPT-OSS. For most business tasks you do not need a trillion-parameter cloud model. A well-chosen local model delivers practical results with zero privacy risk, and that’s a must to avoid data leaks. We’ve also trained our own model, HomeDock OS 3B Instruct, fine-tuned on our entire documentation. It acts as a built-in copilot for the system itself, open-source and available on Hugging Face.

Which digital services can be automated using Local GPT?

With HomeDock OS 2.0 we introduced Prism Window Manager, a full desktop environment in the browser with multitasking, window snapping and everything you’d expect from a real OS. Local GPT lives inside as a native application, you open it like any other app. Organizations use it for

code generation, document drafting, technical analysis, data summarization, internal Q&A, with full export options. If a conversation is too sensitive even for local storage, Privacy Mode keeps everything in memory only, nothing touches the disk. 

Will “Self-hosted AI” become the standard for dev teams in 2026?

The shift is already underway. When AI runs at the OS level alongside Docker containers and CI pipelines, developers can query models against their actual codebase without uploading proprietary code to external services. For regulated industries this is not optional, it is a compliance requirement. We expect 2026 to be the year dev teams start treating local AI as the default for anything involving sensitive code. The momentum around projects like OpenClaw is a clear signal, not just for basic queries, but for full-blown AI agents with complete access to the machine. We plan to bring it to our App Store once their security model matures enough. 

What share will private local AI servers have compared to public clouds in five years?

Public clouds aren’t going anywhere for consumer services and large-scale training. But for enterprise inference? We see 30 to 40% moving to private infrastructure by 2030. Hardware costs aren’t exactly dropping, anyone who’s priced RAM lately knows that. But open-source models are getting remarkably good and remarkably fast. Local deployment is becoming less of a preference and more of a legal reality. That’s where HomeDock OS lives. We want running your own private cloud and local AI to feel as simple as signing up for a SaaS product. We think we’re getting there, closing the gap.