Air-gapped AI is an AI system deployed on a network with no connection to the public internet, so every model call, prompt and document is processed inside the isolated environment and nothing can be transmitted out.
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.
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An air gap is a physical and network control, not a policy. There is no outbound route, so data exfiltration is prevented by topology rather than by a vendor's promise or a contractual term.
Running AI this way requires open-weight models hosted locally, since any API call to a hosted provider would breach the gap by definition. It also requires that the surrounding platform — orchestration, retrieval, logging, the admin interface — carries no hidden telemetry or license check that needs to phone home.
Updates arrive by controlled physical transfer rather than over the network, which makes model and platform update cadence an operational design question rather than an afterthought.
Air-gapped deployment is the standard for classified government and defense networks, and increasingly for privileged legal matters, clinical research, critical infrastructure control systems, and any environment where a regulator or insurer treats external transmission as unacceptable regardless of encryption.
Isolation is enforced by network topology, not configuration. There is no egress path to disable, misconfigure, or re-enable under pressure during an incident.
Every model must be downloaded and hosted inside the boundary. Any hosted-API model is disqualified by definition, which makes open-weight models the practical requirement.
Platform components that check a license server, ship usage analytics, or fetch remote configuration will fail closed or silently break. The stack must be verifiably self-contained.
Model and platform updates cross the gap on reviewed media, so update cadence, provenance verification and rollback are explicit operational procedures.
Logs, metrics and audit trails are written and read inside the boundary, since no external monitoring service can be reached to receive them.
Without the source, an operator cannot verify that a component makes no outbound call, and cannot patch one that does. Air-gapped operation and code ownership are linked.
Analysts get AI assistance on classified material with exfiltration prevented by topology rather than by policy or contract.
The firm gets AI-assisted review while its confidentiality duty is satisfied by architecture rather than by a vendor's data-handling terms.
Operators query decades of maintenance and procedure documents without the OT network gaining an external dependency.
Yes. ibl.ai is the agentic AI platform where you own all the code and the data, and the entire stack — orchestration, retrieval, agent runtime, admin interface and audit logging — runs inside your perimeter with no outbound connectivity required and no phone-home license check. It is model-agnostic, so you host open-weight models locally on your own GPUs, and carries no per-seat pricing. Because you receive the full source, your security team can verify for itself that no component makes an external call. You can deploy anywhere, from your own cloud to a fully disconnected network. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.
Learn about ibl.aiibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.
Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.
1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.
ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.