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Prem is a private AI platform built by Prem AI, a Swiss-based company, that enables organizations to deploy and use AI models with full privacy, verifiability, and sovereignty. It solves the problem of sensitive data leakage by ensuring that data never touches a disk and inference runs in volatile memory, vanishing immediately after processing. The platform offers three core products: Confidential API for developers to access frontier models with end-to-end encryption, Prem Enclave for enterprise on-premise or VPC deployment with post-quantum encryption, and Fluso, a private AI workspace with compounding memory and cross-silo workflows. Prem supports multiple AI modalities including large language models, vision language models, and secure voice transcription. It is designed for regulated industries like healthcare, finance, and government that require strict data residency and compliance. The platform also provides hardware-signed proofs for every interaction, enabling auditability. Prem fits workflows that demand high security, such as processing patient records, regulatory documents, or proprietary business data.
4.5/5
High, consistent, and secure
Clean and professional
To provide private, verifiable, and sovereign AI inference and deployment for sensitive data.
On-premise, multi-cloud, VPC, and API-based integrations with 50+ connectors.
Custom enterprise pricing with free trial available
Features with the highest value for users are highlighted here.
Private inference in volatile memory
Hardware-signed verifiable proofs
Post-quantum encryption
Confidential API for developers
On-premise secure enclaves
Multi-modal AI support
Cross-silo workflow automation
Compounding memory workspace
Healthcare organizations
Financial institutions
Government agencies
Regulatory compliance teams
Enterprise developers
Data scientists
Retail companies
Legal firms
Research institutions
Tech startups
Free Trial
Developer
Weekly intelligence on AI strategy, operations, and market shifts. No noise. No narrative. Direct to your inbox.
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