Fully private AI infrastructure for organizations that can't send data to a third-party API - deployed, secured, and operated within your own environment.
Some organizations - regulated banks, government agencies, healthcare providers - can't send sensitive data to a third-party AI API, full stop. Private AI is Advatech's practice for those cases.
We design and deploy AI infrastructure entirely within your own environment or a dedicated private cloud, with no data ever crossing to a shared third-party service, while still delivering real AI capability.
AI compute and model deployment entirely within your own data center or private cloud.
Fully isolated deployment for the highest-sensitivity environments, with no external connectivity.
Architecture designed to meet regulatory requirements around where data is processed and stored.
Open-source models hosted and served entirely within your security boundary.
Model customization performed without your data ever leaving your environment.
Full control over who can access the AI system and complete audit logging of usage.
Data sovereignty and security requirements defined precisely.
Private or air-gapped infrastructure designed to meet those requirements.
AI models deployed entirely within your environment, with no external data flow.
Ongoing operation with full access control and audit logging.
It can - air-gapped deployment is available for the highest-sensitivity environments - but private AI more broadly means data never leaves your security boundary, which can also be achieved within a connected but isolated private cloud.
Open-source models (Llama, Mistral, and similar) are the standard choice, since they can be hosted entirely within your own infrastructure without a third-party API dependency.
Infrastructure cost is higher than a pay-per-call API, but for organizations where sending data externally isn't an option regardless of cost, that comparison isn't the relevant one - we scope infrastructure cost transparently upfront.