GDPR
Avoid exposing personal data to third-party public APIs.
On-Premise AI · Private AI
Private AI on your own server
We design and implement on-premise AI solutions for businesses in Spain. A private alternative to ChatGPT and Copilot: same capabilities, data under your control, GDPR compliance from day one.
Regulatory context
The European regulatory framework is changing the rules
Many AI tools used without governance today will become unviable for processing sensitive information, contracts, client data or internal files.
AI Act
Traceability, operational control and governance of the AI system.
ENS
Mandatory for public sector entities and their technology providers.
DORA
Banking: operational resilience and reduced dependency on critical third parties.
The problem
Why public AI is not enough
Structural lock-in
Models change at someone else's discretion. Prices rise, terms shift and migration is costly.
Inability to audit
There is no way to prove what was processed, where, with which model version or why.
Regulatory non-compliance
Growing use of public AI in conflict with GDPR, AI Act, ENS and data protection authority guidelines.
“The alternative is not to give up on AI. It's to run it within your own perimeter.”
The organisation decides which models to use, where they run, what data feeds them and who can access what — with full traceability and no critical external dependencies.
What governed AI means
Three non-negotiable principles
01
Sovereignty
Models run within the perimeter the organisation defines: on-premise or sovereign European infrastructure. No data leaves.
Your information does not train others.
02
Traceability
Complete log of what was processed, with which model, at what time and by whom.
Real audit capability before any regulator.
03
Compliance
Architecture designed from the ground up to comply with GDPR, AI Act, ENS and data protection authority guidelines.
Governance is part of the design, not an afterthought.
Practical applications
Private alternatives to the tools you already use
Private ChatGPT for your business
Private alternative to ChatGPT and Microsoft Copilot. LLM deployed on your infrastructure: same capabilities, your data under your control, no per-user licensing costs.
Private legal assistant
Private alternative to Harvey or Legora. AI trained on your documents, never leaving the firm.
Private code copilot
Alternative to GitHub Copilot with a local or sovereign model. No proprietary code leakage.
Process automation
n8n or Make on your own infrastructure. Workflows with local LLM, no external APIs.
WorkOps — Document copilot
Native AI work ecosystem in Word and browser. Private knowledge bases for your firm.
FAQ
Everything about on-premise and private AI for businesses
What is on-premise AI or AI on your own server?
On-premise AI means deploying artificial intelligence models directly on your company's servers, instead of using third-party cloud services. The model runs on your infrastructure: data never leaves your perimeter, you have full control over the system and you are not dependent on external providers to operate.
Can an SME have its own private AI?
Yes. The cost of open source models has dropped dramatically over the past two years. Today it is perfectly viable for a medium-sized company to have its own LLM — such as Llama, Mistral or Qwen — deployed on its own servers or on European cloud. The entry point is not a supercomputer: it is a well-configured server with the right models for the use case.
What is the difference between cloud AI and local AI without internet?
Cloud AI (ChatGPT, Copilot, Claude.ai) processes your data on third-party servers, outside your control. Local AI runs on your infrastructure: your documents, queries and data never leave your network. Local AI is slower for general tasks but is the only option when confidentiality and regulatory compliance are critical.
How do you install a language model on your own server?
The process involves selecting the right model for the use case (size, language, specialisation), configuring the necessary infrastructure, deploying the model with appropriate orchestration tools (Ollama, vLLM, LangChain) and connecting it to your company's data sources. At Galileo Studio we handle the entire process, from selection to production deployment.
Does on-premise AI comply with GDPR?
It is the architecture that best complies with GDPR for processing sensitive data. By running the model on your infrastructure, no personal data is transferred to third parties or to countries outside the EU. We also design the system with full traceability — a record of what was processed, when and by whom — making it easy to demonstrate compliance in any audit.
How much does implementing private AI in a business in Spain cost?
The cost depends on the use case, usage volume and existing infrastructure. A basic implementation for a small team can start from a few thousand euros. A full platform with multiple agents, knowledge bases and automated workflows requires greater investment. The most effective approach is an initial consultation to identify the highest-impact use case and estimate the real return.
Let's discuss your project
We design software and AI solutions that optimize processes, reduce costs, and scale with your business.
Let's discuss your project