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Run sovereign AI on open-weight models you own

Why Sovereign AI with Open-Weight Models

Run sovereign AI on open-weight models you own.

Sovereign AI means running capable AI on your own infrastructure, grounded in your own data, so the information never leaves your business. Open-weight models are what make that possible. These are capable models you can download, run on your own hardware, and keep, from families like Meta's Llama, NVIDIA's Nemotron, Mistral, and Alibaba's Qwen. Because you hold the model, your data stays inside your walls, your running costs become predictable, and you can move to a cheaper or newer model whenever one arrives instead of being tied to one supplier's prices. The catch is that a downloaded model on its own does nothing useful. The value is in picking the right one for the job, hosting it where your data stays put, grounding it in your documents, and putting the governance around it. That's the work we do. We match the model to your task, run it inside your perimeter, and set the rules and data residency around it, so you get the productivity without handing your edge to someone else.

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Capabilities

What we build with open-weight models

01

Model selection for the job

Choosing from the open families, like Meta's Llama, NVIDIA's Nemotron, Mistral and Alibaba's Qwen, based on your task, your hardware, and your budget, with the reasoning and the licence check written down.

02

Private and on-premises hosting

Deploying the open-weight model on your own servers or a private Australian cloud region, so nothing leaves your perimeter and your data residency is yours to prove.

03

Grounding on your own documents

The model connected to your files and systems through retrieval, so answers come from your material and cite it, with access scoped to who's cleared to see what.

04

Fine-tuning where it pays

Adapting an open model to your domain, terminology, and examples where a job is repetitive and high-value enough to earn it, so the output fits your business.

05

Governance, residency, and no lock-in

Plain usage rules, access controls, and retention and residency settings configured within your Privacy Act obligations, with your data and workflows kept separate from any one model so a later swap is a choice, not a rebuild.

Where this leaves you stuck

You already know AI can help your team, but the moment you look closely the risk shows up. Half your people are probably using public chat interfaces already, pasting in whatever they need to get the job done, and nobody has written down what’s allowed. Client files, contracts, and pricing are leaving the building without a record. Meanwhile the tools you’re told to buy all point the same way, toward sending more of your data to someone else’s model and paying more as you use it more. You want the productivity. You don’t want to give away the very thing that makes your business yours.

Open-weight models are the way out of that bind, but they arrive with their own confusion. You’ve heard the names, Meta’s Llama, NVIDIA’s Nemotron, Mistral, and Alibaba’s Qwen, and been told they’re free to download and run. That part is true. The hard part is turning a downloaded model into AI you can actually trust with your work, running where your data stays put.

Why the model alone under-delivers

It’s tempting to think an open-weight model is a simple swap. Download a capable model, run it, and stop paying a subscription. The model really is the cheap and easy part now. These families are strong enough for most business work. But a raw model knows the public internet, not your business. Ask it your refund window or your standard rates and it invents a plausible average that someone has to catch.

The work that makes an open model pay off, and makes your AI genuinely sovereign, is everything around it. Three things separate AI you own from AI you just rent, and none of them come with a download.

Your data has to stay yours. The value of AI on your business comes from feeding it your real information, but that’s exactly the data you can’t afford to leak. So we run the model inside your perimeter, on your own servers or a private Australian cloud region, and ground it in your documents there. Your records inform the answers without ever leaving your control.

You have to be able to change models. When you build everything around one supplier’s product, you inherit their prices, their changes to how the model behaves, and their decision to retire the version you relied on. Because you hold an open model, and because we keep your data, prompts, and workflows separate from it, swapping to a cheaper or better one is a change you choose, not a rebuild you dread.

A person has to stay accountable. Sovereign doesn’t mean automated past the point of judgement. We build these systems so the model does the work and a person signs off on anything that carries weight. That’s what keeps you safe on the days the model gets something wrong.

These are the foundations we insist on. You can read more about them in our approach.

A small Australian team running an open-weight model on their own infrastructure, keeping client data inside the business

How we deliver it

We work in small, reviewable steps rather than one big switch-on, so you keep control and see value early. We start from the job, not the model, because the right open model for a legal summariser isn’t the one for a customer-service agent.

  1. Map where your data goes. We find out how AI is already used across your team and which information must never leave your systems. The honest picture is never zero.
  2. Pick the model for the task. We choose from the open families based on your job, your hardware, your budget, and your data rules, and we write down why. We also check the model’s licence, since most permit commercial use but some carry conditions worth reading first.
  3. Host it where your data stays. We deploy the open-weight model on your own infrastructure or a private Australian region, sized to the job and your hardware, so nothing leaves your perimeter.
  4. Ground it in your documents. We connect the model to the files and systems that hold your answers, with access scoped so people only reach what they’re cleared to see, and answers cite your material. Where a job is repetitive and high-value, we fine-tune the model on your examples and terminology so the output fits your business.
  5. Set the rules and keep a human in the loop. We write a plain usage policy, configure retention and residency within your Privacy Act obligations, and put approval steps where decisions matter. Every choice is documented and versioned.

When open-weight models are the right call, and when they’re not

An open-weight model run as sovereign AI fits when control matters: sensitive data, steady high-volume use, predictable costs, or a real need to avoid being tied to one supplier. Run on your own infrastructure and grounded in your data, it gives you capability that stays inside your business, and for most business work it’s more than capable once pointed at your own records.

It’s the wrong tool when you’d be standing up a data centre to answer a handful of questions a week. For light, general use with non-sensitive data, a governed public tool like ChatGPT is often the simpler and cheaper call, and we’ll say so. The honest position is that most businesses end up with a mix. Open models handle the work that must stay private, and public tools handle the rest. We help you draw that line in the right place rather than selling you one answer for everything.

See the umbrella service in Sovereign AI, and the work it leads into across AI Agents and Integration Services. For a governed public option alongside your own models, see ChatGPT. For sector work, see FinTech & Banking, Healthcare, Insurance, Legal and Professional Services.

Explore further

Read more about our Sovereign AI service and the Open-Weight Models technology.

No stupid questions

Frequently asked.

What is sovereign AI, and how do open-weight models fit in?
Sovereign AI means running AI on infrastructure you control, using models you can hold and change, grounded in data that never leaves your business. Open-weight models are the practical way to do it, because you can download them and run them yourself rather than reaching a provider's model over the internet. You hold the model, so the capability sits where you can govern it.
What are open-weight models?
Open-weight models are AI models whose trained weights are published, so you can download them and run them on your own hardware rather than only reaching them through a provider's service. Examples include Meta's Llama, NVIDIA's Nemotron, Mistral and Alibaba's Qwen. Because you hold the model, your data can stay inside your business.
Is a self-hosted open model as good as ChatGPT or Gemini?
For most business work, yes. An open model you run yourself is capable enough for the vast majority of tasks, and pointed at your own data it often does that work better than a larger public model guessing from the open web. The largest closed models still lead on the hardest general reasoning, so we're honest about which jobs need one and which don't.
Is sovereign AI only for governments and big companies?
No. The big deals make headlines because of who's involved, but the reason behind them, keeping your data and your edge under your own control, applies to any business with information worth protecting. A smaller firm can run a modest open model in-house at a cost that surprises people.
Does this help us meet the Privacy Act?
It helps, because keeping data inside your own systems is the cleanest way to control where personal information goes. Under the Privacy Act 1988 and the Australian Privacy Principles, you're accountable for the information you hold, including where it ends up. We configure residency, retention, and access so you can show where your data lives. It's a tool for compliance, not a substitute for your own legal advice.
Is running our own open model expensive?
Small open models are genuinely cheap to run, and self-hosting turns a metered bill that climbs with every use into a steadier cost you own. It pays off best for firms with steady, heavy use and real reasons to keep control. We'll tell you plainly if a public tool would serve you better for a given job.
Do we need our own servers to run an open-weight model?
Not necessarily. You can run these models on your own hardware or in a private cloud region that you control, including Australian regions for data residency. Small models run on modest hardware, while larger ones need more. We size the setup to the model and the job.
How do we get started?
We start by mapping how AI is already used across your team and which data must never leave your systems. From there we pick the work worth bringing in-house and stand up a model to run it. The first project is a contained piece of work, scoped fixed and in AUD, not an open-ended one.
Take the next step

Find the open model that keeps your AI sovereign

Tell us the work you'd like AI to do and the data that must stay private. We'll recommend the open model that fits, where to run it so your data stays put, and what it would take to set up safely.

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