What is NemoClaw and how does it differ from OpenClaw?

NemoClaw is NVIDIA's open-source reference stack for running agents such as OpenClaw inside OpenShell sandboxes. It controls what an agent can reach. NeMo Guardrails is a separate open-source library that controls what goes into and comes out of the model, and ibl.ai deploys the two together. While OpenClaw provides the agent runtime—orchestration, memory, skills, multi-channel deployment—NeMo Guardrails adds input rails, output rails, topical rails, and dialog rails that intercept model traffic. Guardrails are defined in Colang, a human-readable policy language, and evaluated within your security boundary.

How does NemoClaw prevent jailbreaks and prompt injection?

NeMo Guardrails applies multi-layer detection to every input. Input rails check for known jailbreak patterns, role-playing exploits, instruction override attacks, and novel prompt injection techniques. When a jailbreak attempt is detected, the input is blocked before it reaches the LLM, and the event is logged for security review. This happens in addition to OpenClaw's existing NanoClaw and IronClaw security layers.

What is NVIDIA NIM and why does NemoClaw use it?

NVIDIA NIM (NVIDIA Inference Microservices) provides GPU-accelerated containers for serving LLMs with high throughput and low latency. NemoClaw deploys on NIM to get optimal inference performance on NVIDIA GPUs—whether on-premises, in GovCloud, or in your private cloud. NIM containers are pre-optimized for NVIDIA hardware and support open models like Llama and Mistral.

Can we define custom guardrail policies for our institution?

Yes. Guardrails are defined in Colang—a readable, version-controllable policy language. Your compliance team can define topical boundaries, PII redaction rules, content safety policies, escalation triggers, and dialog constraints. Policies can vary by agent role, user role, or deployment context. ibl.ai configures initial policies during engagement and trains your team to maintain them.

Does guardrail evaluation send data to external services?

No. All guardrail evaluation happens within your security boundary. NeMo Guardrails runs alongside your agents and NIM containers in your infrastructure—on-premises, in your private cloud, or in GovCloud. No user data, agent interactions, or guardrail decisions are sent to NVIDIA or any external service.

Do we own everything after the engagement?

Yes. All Colang guardrail definitions, agent configurations, NIM container settings, integration code, infrastructure configurations, security runbooks, and training materials are delivered to your repositories. Your team operates independently after knowledge transfer. No ongoing dependency on ibl.ai.

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NemoClaw

NemoClaw agents, sandboxed on your terms

Q: What are NVIDIA NeMo Guardrails?

NeMo Guardrails is NVIDIA's safety framework for LLM applications. It provides programmable rails written in Colang—a domain-specific modeling language. Input rails filter user messages before they reach the LLM. Output rails validate agent responses before they reach the user. Topical rails keep conversations on-topic. Security rails block jailbreaks, prompt injection, and data exfiltration. All rails are version-controllable and testable.

NemoClaw-sandboxed OpenClaw agents deployed with NVIDIA NeMo Guardrails—programmable safety rails, hallucination detection, and GPU-accelerated inference for your organization. On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network. No need to choose build vs. buy — you get both.

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On the ibl.ai cloudStart at $0, usage-based from thereFree tier with renewable monthly credits, no credit card. Move to your own infrastructure anytime, nothing to rebuild.Hosted deployment See pricing

On your infrastructureRun it in your own environmentYour cloud, on-premise or air-gapped, under your control. Backend and infrastructure under a perpetual license.Full code license

Prefer to build together? Our AI Transformation team builds agents and apps with yours, on your systems.

Available on

NVIDIA NeMo Guardrails on agents you own

ibl.ai runs NemoClaw-sandboxed OpenClaw agents with NVIDIA NeMo Guardrails: programmable rails, jailbreak and injection defense, PII redaction, audit logging and network isolation. Deployed through ibl.ai, the whole stack stays in your perimeter.

	NemoClaw deployed by ibl.ai	Hosted agent platforms
Where agents run	NemoClaw deployed by ibl.aiYour GPUs, your network, air-gapped if needed	Hosted agent platformsThe vendor's cloud
Guardrails	NemoClaw deployed by ibl.aiNeMo Guardrails you configure and audit	Hosted agent platformsVendor policy, opaque
Models	NemoClaw deployed by ibl.aiAny, including open-weight on your hardware	Hosted agent platformsThe vendor's models
Pricing	NemoClaw deployed by ibl.aiUsage or flat license; no per-seat fees	Hosted agent platformsPer seat, per month

Your Code. Your Data.Full ownership, zero vendor lock-in

Any LLM, Your ChoiceAir-gapped or any cloud provider’s

85% Lower CostSignificantly cheaper at scale

Engineers IncludedOur team builds it with yours

NemoClaw - OpenClaw AI Agents with NVIDIA NeMo Guardrails for Workforce Development

Deploy OpenClaw AI agents with NVIDIA NeMo Guardrails—programmable safety rails that prevent jailbreaks, block prompt injection, detect hallucinations, and enforce compliance policies across your workforce AI.

ibl.ai combines the open-source OpenClaw agent framework with NVIDIA's NeMo Guardrails engine and NIM inference microservices, giving your organization guardrailed AI agents that run on GPU-accelerated infrastructure you fully own and control.

What This Is

NemoClaw is NVIDIA's open-source reference stack for running agents such as OpenClaw inside OpenShell sandboxes. ibl.ai deploys NVIDIA NeMo Guardrails, a separate open-source library, alongside it, adding programmable safety rails that intercept every input and output. Where OpenClaw provides the agent runtime—orchestration, memory, skills, multi-channel deployment—NeMo Guardrails adds the safety envelope that regulated enterprises require.

NeMo Guardrails uses Colang, a domain-specific modeling language, to define rails declaratively. Input rails filter user messages before they reach the LLM. Output rails validate agent responses before they reach the user. Topical rails keep conversations within approved boundaries. Security rails detect and block jailbreak attempts, prompt injection, and data exfiltration in real time.

ibl.ai deploys NemoClaw on NVIDIA NIM inference microservices for GPU-accelerated model serving, integrates it with your HR and L&D systems, and configures guardrail policies specific to your organization's compliance requirements. Every guardrail definition, every agent configuration, every integration adapter belongs to your organization.

Why NemoClaw for Corporate L&D

Programmable Safety RailsDefine guardrails in Colang—a readable, auditable modeling language. Compliance teams can review and modify safety policies without touching agent code. Every rail is version-controlled and testable.

Jailbreak PreventionNeMo Guardrails detects jailbreak attempts—prompt injection, role-playing exploits, instruction override attacks—and blocks them before they reach the LLM. Multi-layer detection catches both known patterns and novel attack vectors.

Hallucination DetectionOutput rails validate agent responses against your corporate knowledge base—policy documents, compliance manuals, training materials. When an agent generates information that contradicts your source documents, NeMo Guardrails flags or blocks the response.

PII & Proprietary Data ProtectionInput and output rails automatically detect personally identifiable information and proprietary business data—employee IDs, salary information, trade secrets—and redact them before they leave your security perimeter.

GPU-Accelerated InferenceNVIDIA NIM microservices serve your LLMs on GPU-optimized containers with high-throughput, low-latency inference. Run open models like Nemotron 3, Llama, or Mistral on your own NVIDIA GPUs, or connect to cloud-hosted models through the same guardrail pipeline.

NVIDIA NeMo Guardrails

Input RailsEvery user message passes through input rails before reaching the LLM. Rails check for prompt injection attempts, toxic language, off-topic requests, and PII. Blocked inputs return a safe, configurable response without consuming LLM tokens.

Output RailsEvery agent response passes through output rails before reaching the user. Rails validate factual accuracy against knowledge bases, detect hallucinated content, redact sensitive data, and enforce tone and format policies.

Topical RailsDefine approved conversation topics per agent. A compliance agent stays within compliance. An onboarding assistant stays within onboarding. Topical rails prevent agents from answering questions outside their sanctioned scope.

Dialog RailsControl conversational flows programmatically. Define required confirmation steps for sensitive operations, enforce escalation paths to human agents, and mandate disclosure statements for regulated content.

Retrieval RailsSecure your RAG pipeline. Retrieval rails validate that retrieved documents match the user's access level, filter out irrelevant chunks, and prevent agents from surfacing restricted content to unauthorized users.

Colang Policy LanguageGuardrails are defined in Colang—a human-readable, version-controllable modeling language. Compliance officers can review rail definitions like documentation. Engineers can test rails like code.

Enterprise Hardening by ibl.ai

Security Patching & CVE Monitoring

We monitor both OpenClaw and NeMo Guardrails security advisories and apply patches before they reach your production environment.

Our team tracks CVEs across the full NemoClaw stack—agent runtime, guardrails engine, NIM containers—and manages updates aligned with your change management process.

Role-Based Access Controls

Deploy agents with granular permissions tied to your identity provider. Employee agents access different data than manager agents.

Guardrail policies vary by role—customer-facing agents have stricter topical and content rails than internal analytics agents. All enforced at the infrastructure level.

Audit Logging & Compliance

Every agent action, guardrail trigger, blocked input, filtered output, and tool invocation is logged to your SIEM or logging infrastructure.

SOC 2/SOX/HIPAA-compliant by design, with configurable retention policies. Guardrail audit trails provide evidence for compliance reporting.

Network Isolation & Data Boundaries

Agents and NIM inference containers run in isolated network segments with strict egress controls. Employee data never leaves your perimeter.

Guardrail evaluation happens within your security boundary—no data sent to external services for safety checks.

Defense-in-Depth Security

A NemoClaw deployment combines separate security layers: OpenShell sandboxing and network policy, OpenClaw's own application-layer controls, NeMo Guardrails' input/output filtering, and ibl.ai's enterprise hardening.

Each layer is configured separately, so a failure in one does not remove the others.

Enterprise System Integrations

HRIS Platforms

Connect NemoClaw agents to Workday, SAP SuccessFactors, BambooHR, or ADP.

Agents query employee profiles, training history, and certifications—all through guardrailed interactions that enforce topical boundaries and redact PII.

Learning Management Systems

Integrate with Cornerstone, Docebo, Absorb, or Degreed.

Retrieval rails ensure agents only surface content the user is authorized to access. Topical rails keep coaching agents within their approved domain.

Compliance & Knowledge BasesConnect agents to policy repositories, compliance training systems, and corporate knowledge bases. Output rails validate agent responses against source documents to prevent hallucinated compliance guidance.

Identity & Access ManagementIntegrate with Azure AD/Entra, Okta, or your SAML/OIDC identity provider. Agent permissions and guardrail policies inherit from your existing role hierarchy—no separate identity management required.

Deployment Options

On-Premises with NVIDIA GPUsFull deployment on your corporate infrastructure with NVIDIA NIM containers running on your GPU servers. Kubernetes orchestration with Terraform IaC. Complete network isolation and maximum inference performance.

Private Cloud (Your AWS/Azure/GCP Account)Deploy in your own cloud tenancy with GPU instances, VPC isolation, private endpoints, and your encryption keys. NIM containers scale with demand. We configure; you own the accounts and the data.

Hybrid (On-Prem GPUs + Cloud Burst)Baseline inference on corporate GPU servers, burst to cloud GPU instances during peak demand. Guardrails evaluate consistently across both environments. Secure tunnels between environments.

What You Own

NemoClaw deployment with all guardrail policies, agent configurations, and security settings documented

Colang guardrail definitions—input rails, output rails, topical rails, dialog rails—in version-controlled repositories

Agent definitions, tool schemas, and system prompts alongside their guardrail policies

NVIDIA NIM container configurations for GPU-accelerated inference

Enterprise system integration adapters with full source code

Infrastructure as Code (Terraform/Helm) for repeatable deployments including GPU provisioning

Guardrail audit dashboards, monitoring configurations, and alerting rules

Security runbooks covering both agent incidents and guardrail policy updates

Engagement Model

Security & Guardrail Assessment (1-2 weeks):Evaluate your infrastructure, SOC 2/SOX/HIPAA requirements, and integration landscape. Define security baselines, guardrail policies, and approved topic boundaries for each agent role.

Hardening & Guardrail Configuration (3-6 weeks):Apply enterprise security, configure NeMo Guardrails with Colang policies, deploy NIM containers, build HRIS/LMS integrations, and establish guardrail audit logging. Deploy to staging for validation.

Agent Development & Rail Testing (2-4 weeks):Build your first set of guardrailed agents—onboarding coaches, compliance assistants, skills-gap analyzers. Red-team test guardrails against jailbreak attempts, prompt injection, and data exfiltration.

Production Launch & Training (1-2 weeks):Controlled rollout with guardrail monitoring dashboards. Knowledge transfer to your team for ongoing agent development, guardrail policy updates, and NIM operations.

Get Started

Architecture Review:Free 30-minute session to assess your infrastructure readiness, GPU capacity, and guardrail requirements.

Proof of Concept:Deploy one guardrailed agent with HRIS/LMS integrations and NIM inference to validate the approach before broader investment.

Enterprise Deployment:Full-scale NemoClaw infrastructure with comprehensive guardrail policies, agent library, NIM containers, monitoring, and ongoing support.

What our partners say about us

Case Studies

Chris Gabriel | Google

Case Studies

Lorena Barba | George Washington University

Case Studies

Dr. Juana Mendenhall | Morehouse College

Juile Diop | MIT

Adam Tetelman | Nvidia

Jason Dom | American Public University System

Benjamin Breyer | Columbia University

Ken Fujiuchi | SUNY

Erika Digirolamo | Monroe College

David Flaten | SUNY

David Vise | Modern States Education Alliance

Linda Wood | ARM Institute (U.S. Department of Defense)

Case Studies

Chris Gabriel | Google

Case Studies

Lorena Barba | George Washington University

Case Studies

Dr. Juana Mendenhall | Morehouse College

Juile Diop | MIT

Adam Tetelman | Nvidia

Jason Dom | American Public University System

Benjamin Breyer | Columbia University

Ken Fujiuchi | SUNY

Erika Digirolamo | Monroe College

David Flaten | SUNY

David Vise | Modern States Education Alliance

Linda Wood | ARM Institute (U.S. Department of Defense)

Frequently Asked Questions

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Run NemoClaw on your own terms

Start free on our cloud today, or take the whole codebase into your own infrastructure. Either way our engineers work alongside your team, and you own all the code and the data.

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