Your hardware. Your AI.
For most companies, cloud AI is the right answer. For some, it's not an option: defense work, regulated industries, contracts that forbid data leaving the building, or leadership that simply won't put company knowledge on someone else's infrastructure. Own is the program for that reality. We deploy open-weight models on your hardware, in your network, under your keys, and build the same systems around them: assistants, automations, document intelligence. State of the art where the state of the art isn't allowed to leave the premises.
Before any hardware is ordered, we map what your contracts, regulators and internal policies actually require: what must stay on premises, what may touch a private cloud, and where the real red lines are. Often the answer is a hybrid, and knowing that early saves serious money.
Open-weight models differ wildly in what they're good at. We test the candidates on your actual tasks, your documents, your language, and recommend the setup where capability meets your hardware budget honestly.
Sizing, procurement guidance and installation: GPU servers or workstations, inference servers, load handling, backup. Deployed in your network, hardened with your IT, running without an internet dependency.
A local model alone is a brain in a jar. We build the working layer around it: document search over your archives, assistants with access to internal systems, the automations that make the hardware earn its cost.
Role-based access, audit logs, network isolation where required. Reviewed with your IT and, where relevant, your client's security officers, before go-live rather than after their objection.
Open models improve every few months. We keep the deployment current: new model versions tested on your benchmarks, swapped in when they win, with your team trained to run the stack day to day.
Every prompt, every document, every answer stays inside your network.
A setup your auditors, regulators and clients' security teams can sign off.
AI on the work that was excluded from it: the classified, the confidential, the contractual.
No API bills, no rate limits, no vendor decision that can switch your AI off.
The documents where AI would help most, contracts, designs, client records, are exactly the ones no policy will ever allow into a cloud. So the biggest opportunity sits untouched.
Defense clauses, public tender terms, client NDAs: it doesn't matter what the cloud provider promises about privacy. The clause says the data doesn't leave, so the data doesn't leave.
Someone proposed ChatGPT, security blocked it, and that was the end of AI in the company. The refusal was correct. The missing part was the version that would have passed.
Models, GPUs, inference servers, quantization: the vocabulary alone makes it sound like you'd need a PhD on staff. Deployed right, it's infrastructure like any other, run by the IT team you already have.
The policy forbids cloud AI, and employees paste company text into free chatbots anyway, on private phones if they must. A sanctioned internal alternative is the only fix that works.
Everyone assumes on-premises AI costs a fortune. For steady daily workloads, a one-time hardware investment often beats years of per-token API bills, and nobody in the room has done the actual comparison.