Run a full-stack AI platform inside your security perimeter — no data egress, no telemetry, no external dependencies. Ever.
Air-gapped AI deployment means the entire ibl.ai platform — agents, models, data pipelines, APIs, and audit systems — runs exclusively inside your infrastructure. Nothing leaves your environment.
For classified agencies, regulated enterprises, and security-conscious organizations, this is not a feature. It is a prerequisite. Most AI vendors cannot offer it. ibl.ai is built for it.
With 1.6M+ users across 400+ organizations and production deployments on infrastructure ranging from government data centers to private cloud enclaves, ibl.ai delivers enterprise-grade AI capability without ever requiring a connection to the outside world.
Most enterprise AI platforms are SaaS-first. Your data travels to vendor clouds for inference, fine-tuning, and logging. Even platforms that claim "private deployment" often embed telemetry, license checks, or model API calls that reach external endpoints. For organizations operating under ITAR, FedRAMP, HIPAA, or classified mandates, this is a disqualifying risk.
The result is a painful tradeoff: accept the security exposure and use modern AI, or lock down your environment and fall behind. ibl.ai eliminates that tradeoff entirely. The platform is architected from the ground up to operate with zero external dependencies — no phone-home, no vendor telemetry, no cloud model APIs required.
Many vendors advertise "private" deployments but still route inference requests through external model APIs or send usage telemetry to vendor servers.
Sensitive data — including prompts, documents, and user behavior — leaves your environment without explicit consent or visibility.SaaS-based AI platforms require periodic license validation, update checks, or authentication pings to external servers to remain operational.
In disconnected or classified environments, the platform simply stops working — creating mission-critical availability failures.Platforms tied to proprietary cloud models (GPT, Claude, Gemini) cannot function without internet access to those provider endpoints.
Organizations in air-gapped environments are locked out of modern LLM capabilities entirely, forcing reliance on outdated or inferior tooling.When AI processing occurs outside your perimeter, you lose the ability to log, audit, and review every agent action within your own security systems.
Compliance audits fail. Incident response is blind. Regulatory obligations cannot be met.SaaS AI vendors hold your workflows, fine-tuned models, and integrations hostage. Switching means rebuilding from scratch.
Organizations are trapped in vendor relationships that conflict with their security posture, with no viable migration path.ibl.ai delivers the complete platform — including source code — to your environment. Deployment targets include on-premise bare metal, private VMware or OpenStack clusters, air-gapped AWS GovCloud, Azure Government, or classified enclaves. No SaaS components are required.
Open-weight models (Llama, Mistral, and others) are deployed locally using your GPU or CPU infrastructure. The platform is model-agnostic — you choose which models run, where they run, and how they are updated. No external model API calls are made.
All data ingestion, vectorization, retrieval, and storage occurs within your perimeter. Document stores, vector databases, and knowledge bases are hosted on your infrastructure. MCP connectors link to internal data sources only — no external endpoints required.
Autonomous AI agents reason, plan, and execute entirely within your environment. Code execution sandboxes, API calls, and tool use are scoped to internal systems. Every agent action is logged to your internal audit infrastructure.
Role-based access control, tenant isolation, and identity management are configured against your existing directory services (LDAP, Active Directory, SAML). No external identity providers are required.
Because customers receive full source code ownership, the platform continues operating indefinitely without vendor involvement. Updates are delivered as versioned packages that you validate and deploy on your own schedule.
No telemetry, no license pings, no external API calls. The platform operates in fully disconnected environments indefinitely. Every component — inference, storage, orchestration, and audit — runs inside your perimeter.
Customers receive the complete ibl.ai codebase. You own it. You can inspect it, modify it, and operate it without any ongoing vendor relationship. True sovereignty over your AI infrastructure.
Deploy and serve open-weight LLMs (Llama, Mistral, and others) on your own GPU infrastructure. Switch models, fine-tune locally, and version control your model registry — all without touching the public internet.
Every agent action, user interaction, model call, and system event is logged to your internal infrastructure. Audit logs are queryable, exportable, and integrated with your existing SIEM or compliance tooling.
Model Context Protocol connectors link AI agents to internal data sources — databases, document repositories, internal APIs — without requiring any external network access. Data stays inside.
ibl.ai deploys reasoning agents that execute code, call internal APIs, and take multi-step actions — all scoped to your internal environment. No agent capability requires external connectivity.
Deploy on bare metal, private VMware clusters, air-gapped GovCloud regions, or classified enclaves. The platform is containerized and infrastructure-agnostic, supporting Kubernetes and standalone deployments.
| Aspect | Without | With ibl.ai |
|---|---|---|
| Data Residency | Prompts, documents, and user data routed through vendor cloud infrastructure for inference and logging. Data residency is a contractual promise, not a technical guarantee. | All data — prompts, documents, embeddings, logs — remains exclusively within your infrastructure. Data residency is enforced architecturally, not contractually. |
| Operational Continuity | Platform availability depends on vendor uptime, license server connectivity, and external API availability. A vendor outage or connectivity loss disables your AI operations. | Platform operates indefinitely without any external connectivity. No license checks, no API dependencies, no single points of failure outside your control. |
| Model Access in Disconnected Environments | Platforms tied to GPT, Claude, or Gemini APIs are completely non-functional in air-gapped environments. No internet means no AI. | Open-weight models run locally on your GPU infrastructure. Full LLM capability in completely disconnected environments, including classified networks. |
| Audit and Oversight | Agent actions and model interactions logged to vendor systems. You receive summaries or exports — not ownership of the raw audit trail. | Every agent action, API call, and user interaction logged to your internal infrastructure in real time. Full audit trail ownership, queryable and exportable on your terms. |
| Vendor Dependency and Exit Risk | Workflows, fine-tuned models, and integrations are locked inside vendor platforms. Switching vendors means rebuilding everything from scratch. | Full source code ownership means the platform is yours permanently. No vendor relationship required to keep it running. Exit risk is zero. |
| Compliance Posture | Security teams must negotiate BAAs, DPAs, and security addenda with AI vendors — and accept residual risk that vendor practices may not meet your regulatory requirements. | Compliance is achieved architecturally. HIPAA, ITAR, FedRAMP, NERC CIP, and similar frameworks are satisfied by the fact that data never leaves your environment. |
| Customization and Control | Platform behavior, model selection, and update cadence are controlled by the vendor. You consume what they offer, on their timeline. | Full source code ownership means you control every aspect of the platform — model selection, update timing, feature configuration, and infrastructure choices. |
Prompts, documents, and user data routed through vendor cloud infrastructure for inference and logging. Data residency is a contractual promise, not a technical guarantee.
All data — prompts, documents, embeddings, logs — remains exclusively within your infrastructure. Data residency is enforced architecturally, not contractually.
Platform availability depends on vendor uptime, license server connectivity, and external API availability. A vendor outage or connectivity loss disables your AI operations.
Platform operates indefinitely without any external connectivity. No license checks, no API dependencies, no single points of failure outside your control.
Platforms tied to GPT, Claude, or Gemini APIs are completely non-functional in air-gapped environments. No internet means no AI.
Open-weight models run locally on your GPU infrastructure. Full LLM capability in completely disconnected environments, including classified networks.
Agent actions and model interactions logged to vendor systems. You receive summaries or exports — not ownership of the raw audit trail.
Every agent action, API call, and user interaction logged to your internal infrastructure in real time. Full audit trail ownership, queryable and exportable on your terms.
Workflows, fine-tuned models, and integrations are locked inside vendor platforms. Switching vendors means rebuilding everything from scratch.
Full source code ownership means the platform is yours permanently. No vendor relationship required to keep it running. Exit risk is zero.
Security teams must negotiate BAAs, DPAs, and security addenda with AI vendors — and accept residual risk that vendor practices may not meet your regulatory requirements.
Compliance is achieved architecturally. HIPAA, ITAR, FedRAMP, NERC CIP, and similar frameworks are satisfied by the fact that data never leaves your environment.
Platform behavior, model selection, and update cadence are controlled by the vendor. You consume what they offer, on their timeline.
Full source code ownership means you control every aspect of the platform — model selection, update timing, feature configuration, and infrastructure choices.
Full AI capability at classification levels up to TS/SCI with zero data exposure risk and complete audit trails for oversight compliance.
Meets FISMA, FedRAMP, and ITAR requirements without architectural compromises or security exceptions.
HIPAA compliance by design — PHI never leaves the covered entity's environment, eliminating BAA complexity and breach exposure.
Eliminates regulatory risk from cross-border data flows and third-party model provider exposure in sensitive financial workflows.
Meets NERC CIP and ICS security requirements while enabling modern AI capabilities in environments where connectivity is prohibited.
Eliminates ethical exposure from sending privileged communications to third-party AI vendors. Full privilege preservation.
ITAR and EAR compliance without restricting AI capability. Technical data never transits commercial cloud infrastructure.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.