OLLM.COM

Introduction to OLLM.COM

OLLM.COM is a privacy-first AI gateway that aggregates multiple large language model (LLM) providers behind a single API. It is designed for organizations and developers who require strong privacy guarantees, data control, and the ability to choose between standard zero-retention infrastructure and confidential computing.

The platform uses a zero-knowledge architecture that enforces zero data visibility, zero data retention, and no training use of customer inputs. For workloads that require encryption-in-use, OLLM.COM supports confidential computing on trusted execution environments (TEEs) such as Intel SGX and NVIDIA confidential computing, and provides cryptographic proof that requests were processed inside a TEE.

Key Takeaways

• Single API that routes to a curated catalog of LLMs across multiple providers
• Zero-knowledge design: no data visibility, no data retention, and no training use of customer inputs
• Choice of security modes: standard zero data retention or confidential computing with encryption-in-use
• TEE support (e.g., Intel SGX and NVIDIA) with cryptographic proof of enclave processing
• Data remains encrypted during processing, in addition to in transit and at rest
• Works with common developer tools: Roo Code, Cline, Cursor, Windsurf, VS Code, and Replit
• Model catalog includes options such as DeepSeek 3.2, GLM 4.6, Qwen3, and GPT-OSS-120B; additional models (e.g., GPT5, Claude 4.5, Grok 4, Gemini 3) are listed as “coming soon”
• Usage-based pricing with credits and real-time scaling

How OLLM.COM Works

OLLM.COM provides a unified API to select a model and a security mode for each request. In the standard mode, the platform enforces zero data retention and prevents training use while maintaining encryption in transit and at rest. In confidential computing mode, requests are processed inside a trusted execution environment, enabling encryption-in-use so plaintext is not exposed to the host.

After processing in a TEE, OLLM.COM provides cryptographic evidence that the request executed inside the enclave, enabling verifiable privacy. The platform integrates with popular development tools, allowing teams to adopt the gateway without introducing new IDEs or custom setup. OLLM also references an “Origin” capability for intelligent automation and persistent context management to support ongoing development workflows.

Deployment Options Overview

Core Benefits and Applications

• Privacy-preserving AI access: Route to multiple LLMs while maintaining zero data visibility and zero retention.
• Verifiable processing: Obtain cryptographic proof that requests were handled within a TEE.
• Consistent integration: Use a single API and familiar dev tools to test, compare, and deploy models.
• Flexible security posture: Select standard ZDR for general needs or confidential computing for encryption-in-use.
• Example use cases: internal assistants, coding aids in IDEs, knowledge retrieval pipelines, and multi-model evaluation where data confidentiality is essential.
• Model availability: options such as DeepSeek 3.2, GLM 4.6, Qwen3, and GPT-OSS-120B; additional models are identified as forthcoming.