Machine Learning technologies are revolutionising business, but like any tool they can be misused, and most of the risk is not exotic. It is data leaving your control, outputs that nobody validates, and dependencies that nobody priced properly at the time they were taken on.
We can help you design inference solutions that work for you, without introducing significant risk surfaces to your enterprise.
What is on your machine is yours
Apple silicon has changed the calculation. Inference that never leaves the machine has no egress to manage, no third party holding your data, and no per-token bill that grows in proportion to how well the thing is working. It also never phones home, which is a property you either have or you do not.
We are further ahead on MLX than most, and our MLXPromptCache module is already open source, so you can look at the standard of the work before you engage us on any of it.
Inference you can govern
Where a workload does genuinely need to run somewhere else, it should be as accountable as anything else in your estate. That means observability, access control, human review at the points where a wrong answer is expensive, and a fallback path for when the model is unavailable or simply wrong.
It also means provenance. Models and prompts are artifacts, and they can carry the same attestation and signing as your code, which is the subject of our Supply Chain Assurance work.
Where the obligations are heading
In July 2026 the Commonwealth announced an Office of AI within the Department of the Prime Minister and Cabinet, and a mandatory national standard for artificial intelligence intended to be legislated. National Cabinet is considering the approach and the detail is still being designed, so anyone telling you today precisely what you will have to do is guessing.
The direction is clear enough to plan against. The framework is mandatory rather than voluntary, it is being drawn as a single standard rather than piecemeal by sector, it reaches into what models are trained on and whether consent existed, and it extends to the resources that large-scale inference consumes.
Two things put you in a good position regardless of the final wording. Being able to say where your inference runs, and being able to prove what went into it. If your data never leaves the machine then a significant part of that surface never applies to you in the first place, and if you hold provenance for your models the way you hold it for your code, you can answer the question when it is asked.
We take this seriously rather than treating it as a compliance afterthought, because the alternative is the poor outcomes and the quiet vandalism that come from handing your work to somebody else’s inference and hoping for the best.
On-Device Inference
Apple silicon machine learning, where your data never leaves the machine and nothing phones home.
Governed Inference
Monitoring, access control, human review and model provenance, built in rather than added afterwards.
Proven at enterprise scale across Energy, Banking and Cyber Security, and running today in our own products.