Fully local
Models and data on the same machine or server. No connection needed for processing.
- No data leaves the infrastructure
- Open, self-hosted models, no third-party keys
- The limit is the GPU you have available
On-premise AI
On-premise AI: the work gets done, the data never leaves.
Models that run on your machine or on your server. Documents stay where they already are.
Or write to us: info@ggtechnologies.sm
30+
years of experience
10+
automated factories
100%
developed in Europe
Where we start
The data you would need AI on is the data that cannot leave.
Many companies stop short of AI for one reason: the data they would need to use it on is exactly the data that cannot leave. Medical records, contracts, designs, customer and supplier data.
The fix is less exotic than it sounds: the model runs inside your own infrastructure and the documents never leave it. It is the principle Podz.AI is built on, and the same one we apply in tailored projects.
Based in
Via Marino Moretti 23, 47899 Serravalle, Republic of San Marino.
Development
Software designed and developed entirely in Europe.
Product
Podz.AI, the personal AI workstation, built on the DigiSense® framework.
How it works in practice
Three configurations, depending on where the data must stay and how much power is needed.
Hybrid with anonymisation
The processing stays on-premise. When cloud power is needed, personal data is masked before it is sent and restored in the answer.
- Masking of names, dates and addresses
- Masked fields are replaced before the request leaves your machine
- What may leave is decided case by case
Integration with systems in use
AI reads documents and business systems where they already are, without copying them into an external platform.
- Controlled access to archives
- No data migration to third parties
- Traceability of who asked for what
How we work
The choice between local, hybrid and cloud starts from the data, not from the technology.
01
Classify the data
Which data is involved, who may see it, and which task has to be done.
02
Draw the boundary
Where the model runs, what it sees and what it must never see. This is where the architecture is decided.
03
Install on-premise
Installation inside your own infrastructure, integration and testing with the people who will use it.
04
Maintain
Maintenance, model updates and evolution over time.
FAQ
Frequently asked questions
What machine do you need to run AI locally?
It depends on model size and load, and in practice on how much GPU memory you have. For one person, a Mac with Apple Silicon or a PC with a dedicated GPU. For continuous business use, a server with a GPU. Exact sizing we do together, before any quote.
Is local AI less capable than cloud AI?
On generic tasks, frontier cloud models remain more powerful. On a defined task — reading one type of document, extracting certain fields — the gap narrows considerably, and the confidentiality advantage weighs more.
Can we start local and open up to the cloud later?
Yes, that is the most common setup. You start fully in-house and open to the cloud only for the steps that need it, with personal data masked before sending.
Is anonymisation enough for compliance?
No, and nobody can answer that for you: for GDPR purposes the compliance assessment remains with the data controller. What we document is the mechanism — which fields are masked, what actually leaves, and how you verify it.