Private AI: use LLMs without handing over your data

The fear that blocks most AI projects

Plenty of companies pause AI initiatives for one reason: "we cannot send our data to a third-party model." For regulated sectors, sensitive IP or personal data, that caution is justified.

What "private AI" means

Private AI keeps your data inside your perimeter while still using state-of-the-art models:

• Self-hosted or VPC-isolated models — nothing leaves your environment.
• Retrieval over private data — the model reasons over your documents without those documents being used to train anyone else's model.
• Access control and audit trails — who asked what, and what the model saw.

A real example

We built a private AI system to improve the metadata of one of the world's largest publishing groups. The point was precisely this: boost accuracy and efficiency without exposing sensitive catalogue data to an external service.

The takeaway

The "capability vs confidentiality" trade-off is largely a myth in 2026. With the right architecture, you get the productivity of modern AI and keep full control of your data. For most enterprises, that is the only version of AI worth deploying.