AI that never leaves your network.
A site licence, hardware we configure and ship, and a person who comes and sets it up. No orchestration layer to run, no framework to keep upgrading.
Most teams evaluating AI hit the same three walls: the data cannot leave the building, there is nobody on staff to operate an ML platform, and somebody eventually has to answer for what the machine did. Those are not model problems. They are operations problems, and they are the ones we solve.
Unix is what evolved when people had to share one computer. Isolated processes, permissions, a filesystem everyone could see. The agents were people — nothing about it had to change for AI.
What Business includes
Site licence
Every engineer, every machine, one agreement. No per-seat accounting and no counting who ran what.
Priority support
A named channel with people who wrote the C, not a ticket queue.
FDE & consulting
A forward-deployed engineer inside your repositories, building the pipelines your team will actually run.
You set some of it
Tell us what your team is missing and we build it. It tends to make the tools simpler for everyone else too.
Hardware, configured and shipped
If inference has to stay inside your network, you need somewhere for it to run. We ship Mac minis and Mac Studios with toasted, toastd and the tools already set up. Several machines cluster over Thunderbolt networking, so a model can be larger than the memory in any one box.
It arrives working. Rack it, point toast at it, and nothing leaves the building. No API keys to distribute, no per-token bill, and no egress for a compliance officer to worry about.
$ toasted # starts itself, like appled $ toast "summarise this quarter's incidents" < incidents.md $ toastd -l # local audit log, on your machine
An audit trail that stays yours
Start the daemon as toastd -l and it logs locally. The log lives on your machine and never comes to us. Paired with a local provider or your own API key — where the traffic never touches our servers either — that makes toastd usable as an inference gateway for a practice that has to answer for its records.
A dental office. A law firm. Anyone whose prompts are privileged, who needs a record they can produce, and who has no interest in becoming an AI company to get one.
Alongside it, ito records why each change was made, not just what changed — and nothing is ever deleted. When someone asks what the machine did in March, that is a question with an answer.
Reliability, and why it is not a model choice
A company is made of people who are wrong quite often, and it gets dependable work out of them anyway — through review, a record of why, and the ability to undo. None of that requires anyone to be reliable.
The same arrangement is what you are buying. Our personas span providers: a persona is a name for a problem, and behind it sits whatever solves that problem best right now — a model from any lab, one we trained, or a combination. So three personas reviewing one document are three different training distributions, and an error has to survive all of them. Your team gets that without holding five vendor contracts and rotating five sets of API keys.
And when something better ships, the persona becomes it. Your pipelines do not change.
Training
A day on your own repositories: the six levels, pipes and loops against your real code, .persona and .tools set up for the work your team actually does, and a blunt answer about what to automate and what to keep your hands on.
Most teams leave with three or four pipelines running and a clear idea of which tasks do not belong on the list. The six levels →
Why this costs less to run
- No orchestration layer to operate, monitor or upgrade
- No framework churn — the substrate is Unix, and it does not ship breaking changes
- Nothing your team has to learn that expires; they keep learning Unix
- Local and BYOK inference spend no credits, so cost tracks the work you send us
- Small C binaries, sub-20ms overhead, no Python environment to manage
That combination is the least staff-hungry way we know to run AI in production. It is also the reason a four-person team can operate this without hiring anyone.
How it starts
Usually a seminar, because it is the cheapest way for both of us to find out whether this fits. Teams that carry on from there take a site licence, and the ones with data that cannot move take hardware as well.
Tell us what you are trying to do and what cannot leave the building.
Contact
Sales: sales@linuxtoaster.com
Security: security@linuxtoaster.com