Why we run AI on our own servers — and when you should too

Public AI services are the fastest way to try a language model. For many companies, especially factories and farms, the questions come later: where does our data go, what happens when the internet drops, and what will this cost when the whole team uses it every day?

We run our own models on our own servers. The text on this website was translated into eleven languages by a model running on hardware in our office, and the Mini.G assistant in our app answers from our own GPU server too.

Reasons to run AI on-premise

• Your data stays inside. Production records, recipes, contracts and chat logs never leave your network, and you decide what is stored and for how long.
• It keeps working offline. A model on site still answers when the internet connection goes down — important when it is wired into operations.
• Local languages. Teams that work in Thai, Burmese, Chinese and English can use one assistant and one translation bot in all of them.
• Predictable cost. You pay for hardware once instead of per user, per month. Heavy daily use is where this pays off.

The honest trade-offs

• Upfront investment. GPU hardware costs money before it saves money. For light or occasional use, a cloud service is usually cheaper.
• Someone has to run it. Models need updates, hardware needs monitoring, and the setup needs care. Either you have the skills in-house or you need a partner.
• The very largest models stay in the cloud. Local models are strong for documents, translation, support and operations, but the biggest frontier models still need data-centre hardware.

Where to start

Pick one task that repeats every day and uses internal information: answering questions from manuals and SOPs, translating messages between teams, or summarising daily reports. Measure it for a month, then expand.

We supply the hardware — from a quiet workstation for one office to a rack server for a whole site — and deploy and maintain it for you. You keep the hardware and the keys. Talk to an engineer about your use case.