Some work simply shouldn't leave your control. This is what the private end of the curve looks like in practice.
A single machine runs open models locally — no external API, no data leaving the device. On top of it sits a set of always-on automations: inbox triage, a scheduled daily briefing, and retrieval-augmented answering over a private knowledge base.
How it's built
The design separates a planner from the workers. A lightweight brain decides what should run and when; the local inference server and a handful of small, single-purpose apps do the work. Everything is scheduled by the operating system and stored locally.
- Local inference for open models — strong open weights, served on-device.
- A private RAG index over documents, so the assistant answers from your own material.
- Scheduled automations that run unattended and report in.
Why it matters
It proves that "private AI" isn't a compromise. With the right open models and a disciplined architecture, an organization can get real day-to-day leverage from AI while keeping sensitive data on infrastructure it owns outright.