About
NEAR AI is an AI cloud infrastructure company built around the principle of user-owned, verifiable AI. The platform addresses a critical gap in enterprise AI adoption: the ability to run powerful AI models on sensitive data without compromising privacy or compliance. Every inference request executes inside a Trusted Execution Environment (TEE) powered by Intel TDX and NVIDIA Confidential Computing hardware, ensuring data is encrypted and isolated even from the infrastructure operator. The platform offers an OpenAI-compatible API, making it straightforward for developers to migrate existing workloads or build new applications without rewriting integrations. It supports open-source and custom models, with multimodal capabilities spanning text, image, and voice inputs. Hardware-backed attestation provides real-time, verifiable proof that each request ran in a secure environment — a process completed in under 30 seconds per job. NEAR AI also offers always-on AI agents (including IronClaw and OpenClaw) that operate within encrypted enclaves, ensuring secrets never reach the underlying LLM. Designed for regulated industries such as healthcare, finance, legal, and government, the platform is positioned as a privacy-preserving alternative to conventional cloud AI services. Its cost-efficient architecture eliminates the need for additional data anonymization tooling, reducing operational complexity while maintaining enterprise-grade security.
Key Features
- Trusted Execution Environments (TEEs): Every inference request runs inside an Intel TDX and NVIDIA Confidential Computing secured enclave, ensuring data is encrypted and isolated from all external parties including the infrastructure operator.
- Real-Time Hardware Attestation: Per-job hardware attestation confirms the integrity and security of the execution environment in real time — completing in under 30 seconds — providing cryptographic proof for every request.
- OpenAI-Compatible API: Access private models through a familiar OpenAI-compatible API, making it easy to integrate with existing developer toolchains and migrate current workloads without rewrites.
- Private AI Agents: Run always-on AI agents (IronClaw and OpenClaw) inside encrypted enclaves so that sensitive secrets and context never touch the underlying LLM in plaintext.
- Multimodal Private Inference: Process text, image, and voice inputs in a single platform, all secured at the hardware level with consistent privacy guarantees across modalities.
Use Cases
- Healthcare organizations running diagnostic AI models on patient records without exposing PHI to cloud operators.
- Financial institutions analyzing sensitive transaction data with AI while maintaining compliance with data sovereignty regulations.
- Government agencies deploying AI for classified or sensitive document analysis with hardware-verified isolation.
- Legal tech companies building AI-powered contract review tools that must guarantee client data confidentiality.
- Developers building privacy-sensitive AI agents and applications who need verifiable proof their data never leaves the secure enclave.
Pros
- Hardware-Verified Privacy: Unlike software-only privacy solutions, NEAR AI enforces data isolation at the hardware level, providing independently verifiable security guarantees that enterprises and regulators can trust.
- Minimal Integration Friction: The OpenAI-compatible API allows teams to plug NEAR AI into existing stacks with little to no code changes, dramatically lowering the adoption barrier.
- Cost-Efficient for Sensitive Workloads: Built-in TEE isolation removes the need for additional data anonymization or tokenization layers, reducing tooling costs and operational complexity.
Cons
- Enterprise Pricing Opacity: Pricing details are not publicly listed and require contacting a sales engineer, which can slow down evaluation for smaller teams or individual developers.
- Hardware Dependency: Reliance on specific Intel TDX and NVIDIA Confidential Computing hardware may limit deployment flexibility or introduce latency compared to standard cloud GPU inference.
Frequently Asked Questions
What is a Trusted Execution Environment (TEE) and why does it matter?
A TEE is a hardware-isolated compute environment where code and data are encrypted and protected even from the host operating system and cloud provider. NEAR AI uses TEEs to ensure that sensitive data processed during AI inference is never exposed, providing cryptographic guarantees of privacy.
Is NEAR AI compatible with existing AI integrations?
Yes. NEAR AI exposes an OpenAI-compatible API, so developers can swap in NEAR AI endpoints with minimal code changes to existing applications that already use OpenAI or similar providers.
What models are available on NEAR AI Cloud?
NEAR AI supports open-source models as well as proprietary models like IronClaw and OpenClaw. Custom model deployment is also supported for enterprise customers with specific requirements.
Who is NEAR AI designed for?
NEAR AI is designed for developers, enterprises, and government agencies that need to run AI on sensitive or regulated data — such as healthcare records, legal documents, or financial data — where conventional cloud AI poses unacceptable privacy risks.
How quickly can I get started?
Developers can get API keys directly from the NEAR AI Cloud and deploy private inference in minutes. Enterprise customers with custom requirements are encouraged to contact the sales team for tailored onboarding.
