On-premises deployment
Run models on your own cloud or on-premise hardware, isolating them from the public internet.
Solutions · Privacy and confidentiality
Private AI, built for absolute data confidentiality
We help you deploy powerful large language models on your own infrastructure, ensuring your proprietary data and customer information never leave your control.
A framework for data sovereignty
End-to-end data control
Manage the entire data lifecycle, from ingestion and fine-tuning to inference and logging, within your security perimeter.
Vendor independence
Avoid vendor lock-in and the data risks associated with third-party APIs and their changing privacy policies.
Problem
The privacy risk of public AI APIs
Using third-party models means sending your most sensitive data—customer PII, source code, financial records—to external vendors, creating compliance risks and IP leakage.
Approach
A private AI architecture for the enterprise
- Assess data sensitivity and regulatory constraints
- Design a secure, isolated model hosting environment
- Implement data anonymization and access control
- Establish audit trails and compliance monitoring
Outcomes
What you get
A deployment architecture that meets GDPR, CCPA, and industry standards
Full ownership of your models, prompts, and inference data
A security playbook for managing and operating private LLMs
Ready to build a trusted, private AI capability?
Let's design a deployment strategy that puts your data security and confidentiality first.
Related insights
Articles on data sovereignty, air-gapped deployments and private AI architectures.
Sovereignty
Why AI data sovereignty is becoming a legal obligation in Europe
GDPR, AI Act, CLOUD Act: why hosting your LLMs in Europe is no longer a choice but a legal and strategic necessity.
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Guide
Choosing between local, private and sovereign AI
The three terms get mixed up constantly. Here is a practical framework to decide what your organization actually needs.
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Costs
Cutting your LLM bill without losing quality
Quantization, batching and model right-sizing — the levers that reduce inference spend by an order of magnitude.
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