Introducing NEAR AI Private Inference in Corbits: Verifiable, Private AI Agents for the Enterprise

NEAR AI private inference is now natively integrated into Corbits, a multiplayer workspace where teams run open-source agents together. Corbits teams get the productivity of shared, capable AI agents with a guarantee their model providers can't offer: their data stays private, and they can prove it.

This is what NEAR AI was built to make possible, now running in production against real enterprise workloads.

The problem: adoption or privacy

The productivity gains from AI agents are too significant to walk away from. But until now, capturing those gains meant handing proprietary data to third-party model providers and trusting a privacy policy to hold.

CISOs and compliance teams have been right to hesitate. A promise is not a proof. What enterprises needed was a way to run capable models without exposing the data going into them, and a way to verify that guarantee independently, not accept it on faith.

That is exactly what private inference delivers.

What NEAR AI Provides in Corbits

The integration brings together the three capabilities enterprise-grade compliance requires.

• Private inference via NEAR AI: Instead of routing your data to standard cloud servers, NEAR AI runs models inside Trusted Execution Environments (TEEs) using confidential compute – a sealed region of hardware that even the machine's operator cannot inspect. An independent attestation service cryptographically verifies that your prompts stay private. NEAR AI ships an OpenAI-compatible API with usage-based pricing per million tokens, and dedicated deployments are available for government and financial institutions.

• The IronClaw secure harness: To orchestrate these models safely, Corbits now runs on IronClaw, NEAR's open-source secure agent framework. IronClaw keeps agents inside strict, secure boundaries as they execute complex, multi-user workflows, the harness that lets a whole team share agents without sharing risk.

• Cryptographically signed accountability: In a multiplayer environment where a team shares agents and context, accountability is non-negotiable. Every action a Corbits agent takes, every inference request, tool call, and token cost, is cryptographically signed into a permanent, tamper-evident record. Corbits also supports Trace Commons, the NEAR-incubated open-source initiative for opt-in, verifiable tracing. Privacy and accountability, from the same primitive.

NEAR AI Powering Enterprises 

Confidential compute has been the technically correct answer to enterprise data privacy for a while. What it lacked was a production home, a place where a real company staked its own compliance posture on the attestation holding, day after day, against live data. Corbits is one of the first examples of what the NEAR AI stack can create. A private, orchestrated, verifiable agents workspace that a compliance team will actually sign off on.

Get started

The integration is live inside Corbits today. Teams can run open-source agents together on a bring-your-own-model foundation, backed by cryptographic proof that their data remains their own.

Builders who want to put private inference to work directly can start with the NEAR AI.