For the most sensitive environments — classified networks, clinical systems, trading floors — "the data stays in our cloud tenant" isn't good enough. The requirement is absolute: nothing leaves the network at all.
That is what air-gapped AI delivers. It runs large language models on infrastructure with no outbound internet connectivity, so prompts, documents, and model weights never cross your perimeter.
What air-gapped AI means
An air-gapped deployment has no path to external services after setup. There are no API calls to a model vendor, no licensing callbacks, and no telemetry.
Everything the AI needs — models, vector databases, orchestration, and agent logic — runs locally on your hardware, inside your security boundary.
This is stricter than "on-premise." Some on-premise products still require connectivity for model serving or license validation. A true air-gapped deployment has zero external dependencies.
The architecture, in plain terms
Local model serving. Open-weight models (Llama, Mistral, Qwen, and others) run on your own GPUs via local inference servers such as NVIDIA NIM, Ollama, or vLLM — no external API.
Local retrieval. Your documents are embedded and indexed in a vector store that lives on your infrastructure, so retrieval-augmented answers never send content out.
Local orchestration. The agent layer that plans, routes, and executes runs alongside the models. With a self-hosted, model-agnostic platform, you swap models without re-architecting.
Full ownership. With a full code license, every component is yours to inspect and operate — essential when auditors require source-level review.
Why model choice still matters when you're air-gapped
Air-gapping doesn't mean settling for one model. A model-agnostic platform lets you run several open models locally and route each task to the best fit — reasoning to one, summarization to another.
This is a structural advantage over single-model vendors: even disconnected from the internet, you keep the freedom to choose and switch models on your own hardware.
Who needs it
Air-gapped AI maps directly to the most regulated sectors:
- Government and defense — classified, IL5, and sovereign workloads under NIST 800-53.
- Healthcare — keeping PHI on-premise for HIPAA without relying on a vendor BAA.
- Financial services — client data that must stay on the firm's own servers.
- Legal — privileged matter data that can't transit third-party infrastructure.
The same ownership model runs across all of ibl.ai's solutions, adapted to each sector's controls.
Getting it operational
The hard part is rarely the model — it's integration, performance tuning, and security hardening on isolated hardware. ibl.ai's forward-deployed engineers install the full stack on your servers, optimize it for your GPUs, connect your data sources, and transfer operational ownership to your team.
After knowledge transfer, the system runs independently — no dependency on ibl.ai, and no connection to the outside world.
The takeaway
Air-gapped AI is how regulated organizations get modern LLM capability without ever letting data leave the building. Run open models locally, keep retrieval and orchestration on-premise, own the code, and stay model-agnostic. Start with the self-hosted AI hub or the air-gapped AI architecture.