Prem is an AI infrastructure tool designed for private open-source model development, fine-tuning, and inference. The platform targets enterprise AI developers, IT security and compliance officers, and data engineers in regulated fields like healthcare, finance, and legal operations. Prem allows organizations to run open-source AI workloads while retaining total ownership of their proprietary data and custom model weights. Its architecture relies on hardware-verified encryption, post-quantum cryptographic security, and verifiable hardware-signed attestations to prove that data remains secure during execution. By utilizing a stateless amnesiac processing structure, the system ensures that user inputs and inference requests reside purely in encrypted memory without being logged or stored. Additionally, Prem offers end-to-end encrypted workspaces through Prem App, a fine-tuning environment via Prem Studio, and dedicated Prem API endpoints for integration into external software like electronic health records. The infrastructure is backed by Swiss-hosted data residency to uphold rigorous legal and compliance standards outside global surveillance jurisdictions. Through these privacy mechanisms, Prem enables teams to analyze confidential records, automate compliance checks, and fine-tune proprietary models without leaking trade secrets.
Problem: Financial firms and RegTech companies must analyze massive volumes of sensitive client data and regulatory updates daily. However, using public AI tools risks leaking "Inside Information" or violating strict data residency laws (like GDPR or FADP), as most AI providers log queries and store data for training.
Solution: Prem provides an "amnesiac" architecture where data exists only in encrypted memory during inference. Its Swiss-based hosting ensures data stays outside the jurisdiction of global surveillance alliances, allowing firms to automate compliance without compromising data sovereignty.
Example: A European bank uses Prem App to connect its internal legal documents and transaction logs. The AI agent identifies potential compliance risks across thousands of pages of new regulations, providing a hardware-signed attestation (cryptographic proof) that the data was processed securely and never stored or logged.
Problem: Healthcare providers need AI to summarize patient histories and clinical notes to reduce administrative burnout. However, uploading Protected Health Information (PHI) to a cloud-based LLM is a major security risk and often violates healthcare privacy regulations due to data retention policies.
Solution: Using Prem API, developers can build healthcare applications with "Zero Data Retention." Because Prem utilizes hardware-verified encryption and stateless processing, patient data is processed in real-time and immediately wiped from the system after the inference is delivered.
Example: A hospital integrates the Prem API into their Electronic Health Record (EHR) system. When a doctor requests a summary of a patient's complex 10-year medical history, the data is encrypted, processed by a high-performance open-source model, and the summary is returned in under 300ms. Prem leaves no digital footprint of the patient's records.
Problem: Enterprise companies want to build specialized AI models trained on their proprietary "secret sauce"—such as internal codebases, unique manufacturing processes, or private research. They fear that using standard fine-tuning services will result in their trade secrets becoming part of a provider's base model.
Solution: Prem Studio allows businesses to turn proprietary data into a competitive advantage while maintaining "Sovereign Weights." This means the company owns the resulting model weights entirely, and the fine-tuning process happens within a secure perimeter where the AI provider cannot access the training data.
Example: A specialized engineering firm uses Prem Studio to fine-tune a model on 20 years of proprietary blueprints and technical manuals. The result is a custom "expert" AI that understands their specific engineering language, deployed on dedicated GPUs where the firm retains 100% ownership of the intelligence.
Problem: During Mergers and Acquisitions (M&A), legal teams must review highly confidential contracts, patents, and financial statements. Using standard AI tools for this "due diligence" is prohibited because the leak of a potential deal or trade secret could lead to massive financial and legal repercussions.
Solution: Prem App functions as a "Confidential AI Workspace." It allows legal teams to analyze sensitive documents "off the grid." With end-to-end (E2E) encryption and universal connectors, teams can securely link their private document repositories to a private AI environment.
Example: A legal team creates a secure workspace in Prem App for a sensitive acquisition. They upload thousands of confidential contracts to identify "change of control" clauses. The AI provides a deep strategy analysis, but because of Prem’s Post-Quantum Encryption, the firm is confident that even future computing power cannot crack the secrecy of the deal data.
Target audience: Best for: Enterprise AI developers, IT security and compliance officers, Data engineers in regulated industries
Pricing: Unknown · Categories: Developer Tools
Tags: developer tools, transcriber
Prem is a self-sovereign AI infrastructure platform created for running and training open-source artificial intelligence models with strict privacy controls. It provides hardware-verified encryption, post-quantum security, and stateless memory execution so that data sent for inference or fine-tuning is never retained or exposed to unauthorized parties. The platform serves developers, compliance personnel, and security officers in regulated sectors such as healthcare, finance, and law.
Prem uses a stateless amnesiac architecture where user data and inference queries exist only temporarily within hardware-encrypted memory. Once the AI generates and returns the required response, the data is immediately wiped from the system without creating persistent logs, local caches, or training artifacts. This zero-retention approach ensures that organizations can process sensitive records, including clinical patient notes and confidential financial audits, without violating data retention laws.
Yes, Prem supports custom model fine-tuning through Prem Studio. When companies train open-source models on private codebases, blueprints, or internal research, the fine-tuning process operates within an isolated, encrypted perimeter. Customers retain full ownership of the resulting sovereign model weights. Neither Prem nor external cloud providers can access the proprietary training data, ensuring trade secrets and intellectual property remain protected.
Prem utilizes Swiss-hosted data residency to process and manage AI workloads. By maintaining infrastructure in Switzerland, the platform operates outside the jurisdiction of foreign data surveillance treaties and complies with strict international data protection laws, such as GDPR and FADP. This setup gives regulated organizations legal certainty regarding where their data resides and how inference tasks are processed.