NVIDIA NemoClaw Deployment

Deploy NemoClaw AI agents. Secure, compliant, cost-cutting.

I am James Ferrer. Trained nuclear physicist. I have built three technology companies, including one that shipped products to 79 countries and one that ran a factory entirely through software. I already have NemoClaw running in production.

I deploy OpenClaw AI agents inside NVIDIA NemoClaw sandboxes so businesses can automate employee work tasks and reduce workforce expenses without exposing sensitive data or losing compliance.

$150/hour. First hour free.

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The Platform

What is NVIDIA NemoClaw?

NemoClaw is not a prompt filter. It is not a guardrail library. It is a Linux kernel-level security sandbox built by NVIDIA, released March 2026 as part of the OpenShell project under Apache 2.0.

NemoClaw means your AI agents can access production data without your security team losing sleep. Your agent runs unmodified inside the sandbox, but with enforced privacy controls, network isolation, and policy-based restrictions that the agent itself cannot override. This is runtime-level enforcement, not behavioral prompting.

Landlock filesystem restrictions prevent agents from reading or writing anything outside their approved directories
Seccomp system call filtering blocks the agent from executing unauthorized operations at the kernel level
Network namespace isolation controls exactly which endpoints the agent can reach, and nothing else
Privacy router keeps sensitive queries on your local Nemotron model and only sends safe queries to cloud APIs
Hot-reloadable policies let you change network rules, inference routing, and permissions without restarting the sandbox
Operator approval workflow catches blocked requests and routes them to a human before proceeding
Runs on NVIDIA GeForce RTX, RTX PRO, DGX Station, and DGX Spark

The Gap

NemoClaw is powerful. Misconfiguring it is dangerous.

NVIDIA published the code and the docs. What they did not include is someone to handle NemoClaw implementation for your environment. A single misconfigured policy does not just reduce security. It creates the illusion of security while your agent still has access to everything it should not touch.

Sandbox Misconfiguration

If your Landlock policies are too broad, the agent can read files outside its sandbox. If your seccomp filters are incomplete, the agent can make system calls you thought were blocked. You will not know either of these happened unless you specifically test for them. Most teams do not.

Privacy Router Failures

If the privacy router is misconfigured, your agent sends customer data to a cloud API instead of your local Nemotron model. The query completes. The agent works. But your sensitive data just left your network, and you will not know until someone audits the inference logs.

Policy Design Gaps

Network policies, filesystem access rules, inference routing profiles, and permission inheritance for subagents. These are all hot-reloadable, which means changes take effect immediately. A wrong edit to a live policy file opens a hole in your sandbox while the agent is running.

Integration Blind Spots

NemoClaw sits between your agent and everything else. If the network namespace does not include your approved model endpoint, the agent silently falls back to a default route. If your monitoring is outside the namespace, you have no visibility into what the agent is doing inside the sandbox.

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What I Do

NemoClaw consulting services

Discovery and Assessment

We talk about what your agents are doing, what data they touch, what your compliance requirements are, and whether NemoClaw is the right approach. You get a written assessment of your current OpenClaw setup with specific NemoClaw recommendations. Not a slide deck. A document that names every gap, every risk, and every policy you need. If plain OpenClaw is fine for your use case, I will tell you that instead.

Architecture and Policy Design

I design your complete NemoClaw configuration: Landlock filesystem policies, seccomp filter profiles, network namespace rules, privacy routing tables, and inference profiles for your local Nemotron models. You get a written architecture document with every policy file, every routing rule, and every approval workflow mapped to your specific infrastructure. Implementation does not start until you sign off on the plan.

Deployment and Testing

I deploy NemoClaw on your hardware, configure every sandbox layer, connect your approved model endpoints, set up the privacy router, and wire in your monitoring. Then I test every boundary: I attempt file access outside the sandbox, I send queries that should be privacy-routed, I try network calls to blocked endpoints. You get a test report documenting every rule and every result. Your agent runs inside it unmodified.

Training and Handoff

I train your team to manage NemoClaw policies, approve blocked agent requests, add new inference profiles, hot-reload configuration changes, and monitor agent behavior inside the sandbox. You get versioned policy files, a runbook for common operations, and documentation your team can actually use. When I leave, your team runs it. No ongoing dependency, no retainer required.

The Honest Truth

Can you set up NemoClaw yourself?

Yes. NemoClaw is open source. Apache 2.0. Every line of code is on GitHub.

Here is what that looks like with alpha software that just launched.

Setting up NemoClaw yourself

✕Reading docs for software that shipped weeks ago, with known gaps and missing examples
✕Debugging Landlock and seccomp failures with no Stack Overflow answers and a small community
✕Writing privacy routing policies from scratch with no reference configurations for your industry
✕Configuring Nemotron inference profiles without knowing which parameters affect latency versus accuracy
✕Believing your sandbox is secure because the agent appears to be working, without testing whether the restrictions actually hold

Working with a NemoClaw consultant

✓Sandbox configured correctly the first time, based on your actual agent workload and data sensitivity
✓Privacy routing policies designed around your specific compliance requirements
✓Inference routing tuned for the right balance of cost, privacy, and performance
✓Every sandbox boundary tested and documented before you go live
✓Working system in one to two weeks, with your team trained to manage it

A misconfigured NemoClaw sandbox is worse than no sandbox at all. It gives your security team false confidence while your agents still access data and endpoints they should not reach. The cost of getting this wrong is not a few hours of debugging. It is a compliance violation your team did not know existed until an audit catches it.

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How It Works

NemoClaw deployment in three steps

Audit

I audit your current OpenClaw setup, map your agent workflows, identify what data they touch, and determine the exact NemoClaw policies you need. You get a written plan before anything changes.

Build

I write your Landlock policies, configure your seccomp filters, set up your privacy router, deploy the sandbox on your hardware, and test every boundary. You do not touch it until every restriction is verified.

Handoff

I train your team on policy management, show them how to handle approval requests and hot-reload configurations, and hand over versioned policy files with full documentation. Then it is yours.

I take on a limited number of NemoClaw deployments at a time. If the calendar has open slots, book one.

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Common Questions

NemoClaw FAQ

I test every boundary. After deployment, I run your agent against each configured policy and verify that blocked actions stay blocked, that the privacy router intercepts what it should, and that network namespace isolation holds under load. You get a written test report documenting every rule, every test case, and every result. If something fails, I fix it before handoff. You do not go live on trust. You go live on evidence.

OpenClaw by itself has no sandbox, no privacy routing, and no policy enforcement. If your agents handle customer data, access production databases, or run autonomously for hours, you need controls the agent cannot override. NemoClaw enforces those controls at the Linux kernel level using Landlock and seccomp. It restricts network access, routes sensitive queries through your local Nemotron model instead of cloud APIs, and requires human approval for any action outside policy. If your agents only touch non-sensitive data in a development environment, plain OpenClaw is fine. For anything in production with real data, you need the sandbox.

NemoClaw requires NVIDIA GPUs. Supported hardware includes GeForce RTX PCs and laptops, RTX PRO workstations, DGX Station, and DGX Spark. You need Linux Ubuntu 22.04 or later, Docker, and a fresh OpenClaw installation. For local inference with the privacy router, you run NVIDIA Nemotron models on your own GPU. I spec the exact hardware during the discovery call based on your agent workload and data volume.

NVIDIA labels it alpha, released March 2026. APIs and interfaces may change. But the security primitives underneath, Landlock filesystem restrictions, seccomp system call filtering, network namespace isolation, these are proven Linux kernel features that have been in production across the industry for years. The governance layer works today. What is still coming is the polish, the tooling, and the broader ecosystem. That is exactly why having someone who already runs it matters.

NemoClaw runs OpenClaw agents unmodified inside its sandbox. It also supports Claude Code, Cursor, OpenCode, and OpenAI Codex. Your agents need zero code changes. NemoClaw controls access at the runtime level, outside the agent process. The agent does not know it is sandboxed. It just cannot do things your policies prohibit.

It will happen. NemoClaw is alpha. Your sandbox configuration and policy files are yours, version-controlled and fully documented. Your team can apply routine updates. If an update breaks your configuration, book a follow-up session. I will diagnose the issue, update your policies, re-test, and hand it back. This is not a retainer. It is pay-per-session at the same $150/hour rate.

About

James Ferrer

I am an American-trained nuclear physicist who decided to build companies instead of reactors. I have built three technology companies: one that created and sold products to 79 countries, and one in deep technology software and hardware where I ran a custom factory led entirely by software. I also founded Andrak.ai for enterprise AI.

I have significant experience creating ISO 9001 quality management systems and other high-compliance enterprise processes, including enterprise resource planning and manufacturing execution system automation. I understand what compliance actually means inside an organization because I have built those systems from scratch.

Now I run Express AI, where I serve as companies' AI growth and operations partner, from service businesses up to full-scale manufacturing and industry. I have been deploying OpenClaw since the early versions and started working with NemoClaw as soon as NVIDIA released it. I know the sandbox, the privacy router, the policy engine, and where the documentation has gaps that will cost you time.

If NemoClaw is wrong for your situation, I will say so. I have told companies to run plain OpenClaw. I have told companies they need something NemoClaw does not handle yet. My job is to get you the right solution, not to sell you the one I specialize in.