Prem AI

Prem AI delivers a private, sovereign AI ecosystem designed for organizations that require complete control over their data and model weights. Built on Swiss infrastructure with post-quantum encryption and stateless-by-design architecture, Prem makes it physically impossible for anyone—including Prem itself—to access user data during inference. The platform combines fine-tuning, document analysis, and scalable API inference into a unified stack for regulated industries and enterprises.

• Prem Studio — Fine-tune specialized models on proprietary data with multimodal ingestion, sovereign weight ownership, and one-click deployment to on-premise, hybrid, or AWS-VPC environments.
• Prem App — Analyze sensitive documents and collaborate with AI completely off the grid, with end-to-end encryption and model-agnostic support.
• Prem API — Build scalable, confidential applications using high-performance inference for leading open-source models, with zero data retention and dedicated GPU resources.
• Stateless by Design — Data exists only in encrypted memory during inference and is physically inaccessible to all parties, including Prem.
• Post-Quantum Encryption — Infrastructure secured against future cryptographic threats, with cryptographic proof of privacy guarantees.
• Sovereign Weights — Organizations hold their own encryption keys (HYOK) and maintain absolute control over model weights, whether on-premise or in the cloud.
• Efficient Guardrails — Intelligent safety controls that detect harmful outputs, ensure compliance, and maintain brand safety without sacrificing performance.
• Document Processing — Extract, analyze, and transform information from any document format at scale, converting unstructured data into actionable insights.
• Enterprise Deployment Flexibility — Compatible with On-Premise, Hybrid, AWS-VPC, and Prem Cloud deployments to meet strict regulatory requirements.
• Performance at Scale — Achieves sub-300ms inference latency, 50% inference time reduction, and up to 70% price savings per token compared to general-purpose models.