Private AI
Run private AI models in your own cloud account, in your data centre, or on hardware in your office. You choose how much you own. We set it up, connect it to your systems and hand it over working.
Private AI means running AI models, often called a private or self-hosted LLM, in an environment your organisation controls: your own cloud account, your data centre, or hardware on your premises (an on-premise LLM). Your data is processed there instead of being sent to a shared public AI service, and you decide who can access it.
Your Choice
All three run private models that only your organisation uses. The difference is where the compute sits and who owns it.
GPU capacity in your own AWS, Azure or private-cloud account, set up in your name from day one, so there is nothing to hand over later.
GPU servers you own, racked in your data centre or a colocation facility you lease, on your own network.
Compact AI units installed in your office or branch, beside the cameras, sensors and people that use them.
Mix them, for example devices at each branch with GPU servers in your data centre. The same open models and API run in all three, so you can start in your cloud and move later.
Why Private AI
Client files, recordings and regulated records stay in an environment you control, which supports your data-protection and client obligations.
In your cloud, usage is billed straight to your own account. On-premise, it's a one-off hardware purchase instead of fees per request.
On-premise installations carry on through internet outages and catch up when the connection returns.
Accounts in your name, hardware as your asset, and a handover that lets your team run it without us.
What You Get
One accountable partner from assessment to handover. You don't coordinate cloud consultants, hardware suppliers, installers and developers yourself.
Before You Decide
Straight answers to the questions that decide private AI projects.
Set-up is a fixed figure agreed before you commit. In your own cloud, running costs are billed directly to your account. On-premise, hardware is a one-off purchase you own, with the supplier warranty documented before anything is ordered.
You do. Cloud accounts are in your name from day one, and on-premise hardware is your asset. Your data and applications remain yours.
It stays in your environment and is never pooled into a shared public AI service. On-premise, raw data never leaves the building.
On-premise, the manufacturer warranty covers parts. We diagnose the fault, handle the warranty claim and reinstall the repaired unit for you.
Your team keeps running it. Handover includes administrator access, setup records and step-by-step operating guides. Ownership of the code we write is set out in the contract.
Set-ups in your own cloud are quicker; on-premise adds a site survey and hardware lead times. You get a timeline together with the fixed figure.
Yes. Open models can handle drafting, summarising, document search and image analysis on your own servers or in your own cloud account. We benchmark them on your real work first, so you know what to expect before you commit.
Yes. We size and source rack-mount GPU servers, configure them ready for your racks, and install them in your data centre or a colocation facility you lease, working with the facility's on-site technicians where needed. The servers are your asset, on your network, with the same private API and handover as any other option.
All three run private models that only your organisation uses. In your cloud account there is no hardware to buy and capacity can grow with use. In your data centre you own the servers and get enterprise power, cooling and security. On your premises the data never leaves the building and the system can keep working offline.
How A Project Runs
On-premise projects add a site survey, and nothing is bulk-purchased until one unit has been proven at your site.
What you need AI to do, your data rules and your existing systems.
A clear brief
Your cloud, your data centre or your premises, with a fixed figure.
A fixed figure
Configured in your account, or hardware installed on site.
A running system
Accuracy and speed measured on your own data.
Measured, not promised
Agreed tests passed, team trained, documentation delivered.
Your team runs it, or we do
Deployed into your AWS or Azure account, or your hosting provider. Accounts in your name from the start, with your team given access as it is built.
Open models on dedicated GPU instances, or managed model services such as Amazon Bedrock, inside your account.
Every resource defined in code, so the environment can be reviewed, rebuilt or moved.
Sized and sourced for your models, configured before delivery and racked in your facility or a colocation space you lease.
Connected over your existing network or private links to your sites, behind your firewalls and monitoring.
Your facility provides power, cooling and physical security; InitD installs and supports the AI stack, with the facility's on-site technicians where needed.
Compact GPU workstations with large unified memory. Two units link directly to serve larger models together.
Local NAS with RAID for documents, recordings and model files, sized to your retention period.
High-speed switching between compute and storage, connected to your existing network.
Optional. Small computers beside cameras, microphones or sensors, processing data where it's captured.
# Before: a public AI service client = OpenAI(base_url="https://api.openai.com/v1") # After: your cloud or your hardware, same code client = OpenAI(base_url="https://ai.yourcompany.internal/v1")
In your cloud account or data centre, the same top three layers run there instead of on local hardware, and devices at your sites connect over your network.
Tell us what you need AI to do, where the data must stay and who will run it. We will recommend an option and give you a fixed figure.
Discuss your setup