Prem

Prem is an applied AI research lab and product suite for private, verifiable, sovereign AI. Private workspace, confidential inference API, and tools to train and deploy private models.

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Private AI infrastructure and applications

Prem is an applied AI research lab and product suite focused on private, verifiable, and sovereign AI. The site presents three main products: Fluso, a private AI workspace; Prem API, a confidential multi-modal inference API; and Prem Studio, a platform for training and deploying private, task-specific models.

The platform is aimed at organizations and developers that need to work with sensitive or proprietary data without sending it to a public AI service. Across the site, Prem emphasizes enclave-based execution, end-to-end encryption, cryptographic attestation, and model ownership as the core of its approach.

Core capabilities

Bring-your-own-compute deployment

Prem Enclave installs on existing GPU clusters and turns them into a confidential computing environment without requiring a hardware swap.

Verifiable execution and auditing

The site says every interaction can generate a hardware-signed proof, allowing auditability of who accessed what and when.

Private inference handling

Payloads are decrypted only inside the enclave, with volatile-memory inference described on the homepage and memory isolation called out on Enclave.

Multi-modal confidential API

Prem API offers a unified endpoint for secure voice transcription, vision-language models, and large language models, with OpenAI-compatible integration.

Private model training workflow

Prem Studio covers dataset preparation, finetuning, evaluation, and deployment for task-specific models built from a team’s own data.

Model ownership and self-hosting

Prem Studio states that exported models can be self-hosted, and the homepage frames Prem as sovereign, private, and personalized AI.

Common use cases

Sensitive AI processing
Run document analysis, transcription, or multimodal inference on data that should not leave controlled infrastructure.
Confidential compute deployment
Install enclave-grade AI infrastructure on existing GPU clusters for regulated or jurisdiction-bound environments.
Private model development
Train, evaluate, and deploy task-specific models on internal datasets, then export them for self-hosted use.
Private knowledge work
Use a private workspace to centralize context, connectors, and workflows for day-to-day AI-assisted work.

Pros and Cons

Pros

Supports private inference and model workflows instead of requiring public-cloud AI for sensitive data.
Combines multiple product layers, including a private workspace, confidential API access, and model training/deployment.
Uses enclave-based and attestation-oriented language to describe verifiable execution and auditability.
Supports existing GPU hardware in Prem Enclave, which reduces the need for a hardware replacement.

Cons

The provided pricing page is a not-found page, so public pricing details are not available from the supplied evidence.
The source pages do not include a complete integration catalog or setup documentation, so deployment details are still partial.

FAQ

What is Prem?

Prem positions its products around private, verifiable, and sovereign AI. The site points to offerings for confidential inference, private AI workspaces, and private model training and deployment.

Is pricing public?

Yes. The site presents a contact-sales flow on the homepage and request-access or request-demo calls to action on product pages, but it does not show public pricing on the provided pricing URL, which returns a not-found page.

How do the products differ?

Prem Enclave is described as software that installs on existing GPU clusters in on-premises, cloud, or VPC environments. Prem API is described as an OpenAI-compatible confidential inference API, and Prem Studio is for training and deploying private, task-specific models.

Where can Prem be used?

The site says Prem Enclave supports on-premises, public cloud, and private cloud deployments. Prem API and Prem Studio are presented as developer and model-workflow products built around private inference and private model ownership.

Does the site document setup and integrations in detail?

The source mentions features such as hardware-signed proofs, enclave isolation, end-to-end encryption, OpenAI-compatible integration, and model export/self-hosting. It does not provide a complete implementation guide or connector list on the pages provided.