Private / on-premise AI
Making the most of AI should not force you to hand your data to a third party. We design private or on-premise deployments so your sensitive information stays in your house, under your control, without giving up an ounce of power.
Your data never leaves your control
For many companies, the brake on AI is not the technology: it is trust. If you work with clinical, financial, legal or any sensitive data, the question is fair: where does everything the AI processes end up? Our answer is clear. We can deploy the intelligence in private mode or directly on your own infrastructure.
That means your data stays in your house: on your servers, inside your perimeter, under your rules. We design the architecture with security as part of the blueprint (segmentation, access control, traceability) so you enjoy the power of AI without your sensitive information crossing any border you do not want it to.
Sovereignty without giving up power
Private does not mean limited. The same intelligence that automates processes, watches systems or captures leads can run in a closed environment, tuned to your confidentiality requirements. We help you choose the right model based on your sector, your legal obligations and your risk appetite, without selling you more infrastructure than you need.
And we do not leave you alone with the machine switched on. With 28 years managing critical systems, we take care of the deployment, the security, the maintenance and the evolution of your private AI, just as we watch any other piece of your infrastructure. The sovereignty of your data and the peace of mind of an expert team behind it.
How we approach a private deployment
A private AI project starts on paper, not with hardware. First we analyse with you which data the AI will touch, what obligations your sector carries and what level of isolation you genuinely need: a firm handling case files is not the same as a clinic with medical records. From that analysis comes the architecture: which model, what sizing, what runs inside your perimeter and what (if anything) can stay outside.
Then comes the phased deployment: we build the environment, segment it from the general network, define who accesses what and test it with real cases before signing it off. We document every decision and every configuration, because a private AI that only its installer understands is not sovereignty: it is just another dependency.
On-premise, private cloud or hybrid: deciding with criteria
Not every project needs the same degree of isolation, and forcing the maximum onto all of them adds cost without adding value. So we frame the decision with criteria, not dogma: how sensitive the data is, what your legal framework demands, how much computing power the use case requires and what infrastructure you already have. With those answers, the architecture almost draws itself.
Sometimes the answer is pure on-premise: everything inside your servers. Other times, a private cloud managed by us. And often, an honest hybrid: sensitive work is processed at home while generic work can lean on the outside, with data always classified and the borders clear. We explain the pros and cons of each option before deciding, and the decision is yours.
Infrastructure, security and AI: the same house
An on-premise AI is, first and foremost, infrastructure: servers to size, networks to segment, backups to verify, access to watch. That is exactly the ground SYSBalear has covered for 28 years. We do not subcontract the layer underneath or refer you to another provider: the same team that designs your private AI manages systems, hosting and cybersecurity every day.
It shows in the details that never make it into presentations: server hardening, backups tested with real restores, monitoring that warns before something fails, patching that never gets forgotten. And it shows in proximity: we are in Mallorca, and when a deployment calls for physical presence at your premises, we go.
Frequently asked questions
Does a private AI perform worse than a cloud one?
Not necessarily. Private is not a synonym for limited: the same intelligence can run in a closed environment, scaled to your needs. We help you choose the right model and architecture so you get the power you need with the level of confidentiality your sector demands.
Is this suitable for clinical, legal or financial data?
Yes, that is exactly what it is for. When you handle highly sensitive information, private or on-premise deployment keeps your data inside your perimeter. We design every project with security, access control and traceability, aligned with your sector's obligations.
Do I need to buy servers to run AI on-premise?
Not always. Before proposing any purchase, we assess the infrastructure you already have: in some cases it is enough to use or expand it; in others, dedicated hardware sized to the use case makes sense. Analysis comes first and investment second, never the other way round: we do not sell you iron you do not need.
Who maintains and updates the private AI after deployment?
We do, as part of the service. We handle updates, security, backups and monitoring of the environment, just as we do for the rest of the systems we manage. You keep sovereignty over your data; we make sure the platform stays healthy, patched and watched.
Can I start in the cloud and move on-premise later?
Yes, and sometimes it is the most sensible path. You can start with a managed private deployment to validate the use case and, once proven, move the workload to your own infrastructure. We design the architecture with that portability in mind from day one, so changing house does not mean starting from scratch.
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