Cloud AI has no viable path into classified or sensitive government environments.
The requirement isn't a preference; it's a hard technical constraint. Zero external network calls. No third-party API dependency. No data that crosses the perimeter. Most AI vendors run everything through their own infrastructure. Their "private deployment" options still call home for inference or updates.
4MINDS runs inside your secure enclave. No external calls, by design.
How Defense & Government teams use 4MINDS
Analysts working with classified materials cannot use any system with external network connectivity. 4MINDS deploys with zero external connections: no outbound API calls, no telemetry, no model update traffic from outside the perimeter. The model reads and synthesizes classified intelligence documents entirely within the secure enclave. Ghost weights continues improving the model from corrections and new documents inside the air gap.
LLM-native time series forecasting ingests sensor data from aircraft, vehicles, or infrastructure. The model identifies anomaly patterns that precede failures and produces a maintenance forecast with a written explanation: which asset, what the anomaly pattern looks like, and how much lead time the historical failure signature suggests. One platform, no separate ML forecasting stack.
Agencies with legacy systems need to modernize code without sending any of it to a commercial API. 4MINDS native agentic software engineering runs entirely on-premises. Agents read the existing codebase, understand the architecture, implement features or refactors, run tests, and close the loop. The codebase never leaves the perimeter.
The average federal agency IT modernization project takes three to five years. 4MINDS deploys on Kubernetes: it runs in existing secure infrastructure without a cloud negotiation or a new procurement vehicle. A working deployment against a classified document corpus can be operational in a single sprint.
What your compliance team will ask — and what the architecture answers
4MINDS is not a cloud AI service. Air-gapped deployment means zero external network connectivity, physically or logically isolated, your choice. No telemetry. No phone-home updates. No API calls. The model runs entirely inside your environment and updates from your own data.
4MINDS is a dedicated single-tenant deployment. There is no shared inference layer, no multi-tenant model serving. Your deployment has its own weights, its own data, and its own Kubernetes cluster. No other organization's queries touch it.
Full deployment with zero external network connectivity. Inference, training, and model updates run inside the secure enclave. This isn't "VPC isolation" or "private cloud" where a vendor's infrastructure still gets called. The system has no outbound calls to 4MINDS or any external service after deployment.
Nemotron 3, Qwen, and OSS 120B run on your infrastructure. No vendor API for inference. Your team can inspect the model weights. No black-box model from a commercial provider your security team can't audit.
The continuous fine-tuning loop operates entirely within the air-gapped environment. Sensitive training data, operational doctrine, institutional knowledge, mission-specific terminology — none of it crosses the boundary. The model improves on your data, inside your perimeter.
Every model version passes a quality gate before deployment. Full version history is retained. Instant rollback available if needed. Your team controls when and what gets deployed.
Key differentiators for defense & government
- ›True air-gap capability: No external API calls after deployment. Inference, training, and model updates run entirely within your secure enclave with zero external connectivity.
- ›Open-source model weights, no vendor dependency: Nemotron 3, Qwen, and OSS 120B run on your hardware. Your security team can inspect the weights. No commercial vendor API that calls home.
- ›Continuous institutional knowledge update: Ghost weights trains on new analysis and doctrine changes inside the perimeter. Your model reflects current institutional knowledge, not the training cutoff from deployment day.
- ›Full version control and instant rollback: Every model version is retained with an audit record. Rollback is a single command.
See how 4MINDS handles defense & government requirements.
30-minute technical walkthrough. On-prem deployment. No pitch deck.
Defense contractors and government agencies with classified workloads use 4MINDS because the air-gapped, open-source architecture removes the external dependency question entirely. There's nothing to audit at the vendor, because the vendor isn't running anything.
What air-gapped actually means — and how to tell when a vendor claim does not hold up
"Air-gapped" and "private deployment" are not the same thing. Most enterprise AI vendors offer the second and use language that implies the first. The distinction matters when a misclassification means a classification violation or a supply chain compromise.
Three AI use cases drive demand from defense contractors, intelligence community customers, and federal agencies:
Document exploitation and intelligence synthesis. Processing large volumes of unstructured documents — captured materials, signals summaries, OSINT feeds, technical reporting — requires a model that carries mission-specific terminology, operational doctrine, and institutional knowledge. Commercial LLMs trained on public data do not carry it. Ghost Weights trains on your operational doctrine and analytic products inside the secure enclave. The model reflects current institutional knowledge — not a training cutoff from deployment day.
Mission planning and decision support. Logistics modeling, doctrine retrieval, scenario analysis, operational decision support. The queries, the responses, and the reasoning connecting doctrine to operational context are classified. This must happen inside the secure enclave with no external connectivity — not in a dedicated cloud tenant with a contractual assurance.
Secure software development. Agentic software engineering for classified systems cannot route codebase context through a commercial AI API. 4MINDS' native agentic engineering capability runs on-prem on open-source models, fine-tuned via Ghost Weights on your secure coding patterns. Codebase comprehension and autonomous code generation happen inside your environment.
Why most "private deployment" claims do not deliver air-gap
When a commercial AI vendor says "private deployment," they typically mean: a dedicated tenant in their cloud infrastructure, isolated from other customers. Your queries travel to their servers in an environment contracted to be separate. Their orchestration layer processes your requests on their hardware. Model updates originate from their infrastructure.
That is not an air-gapped deployment.
DCSA requirements for systems handling CUI prohibit transmission to commercial cloud infrastructure without specific authorization. DISA STIGs for software on government networks specify hardening configurations that cloud-dependent architectures cannot meet — their software components require external API calls to function.
4MINDS' air-gapped deployment means: after initial deployment, there are zero outbound network calls. Inference runs on your hardware. Ghost Weights trains inside the enclave. Model updates are generated inside your environment from data inside your environment. 4MINDS infrastructure is not involved after deployment — not as a call-home mechanism, not as a telemetry endpoint, not as an update source.
Open-source models: no black-box supply chain risk
Nemotron 3, Qwen, and OSS 120B run on your hardware. Your security team can inspect the model weights. No proprietary model from a commercial vendor your team cannot audit. No inference dependency that creates supply chain risk if the vendor's systems are compromised.
Supply chain risk management requirements treat opaque software components — including proprietary model weights with unknown training data provenance — as risk factors. Open-source weights are community-vetted, benchmarked by independent researchers, and fully inspectable by your team.
How 4MINDS handles this architecturally
Ghost Weights training, eval gating, and atomic swap all run on infrastructure you control. New doctrine, updated analytic frameworks, mission-specific terminology — the model learns from your data inside your perimeter. Every model version is retained inside your environment. Rollback is a single command. Your team controls the deployment schedule.
The eval gate generates a version record for every update: what changed, what benchmark it passed, when the swap occurred. That record lives inside your environment.
"We're already running a commercial AI tool in a dedicated government cloud instance. What is the gap?"
Dedicated government cloud instances run on vendor infrastructure. Inference happens on their servers. Their orchestration layer calls their APIs. Software updates require connectivity to their infrastructure. A dedicated government cloud tenant is a contractual arrangement, not a technical air gap. 4MINDS on-prem means the software runs on your hardware, on your network, with no technical dependency on external infrastructure. The isolation is architectural, not contractual. Here is the test: if 4MINDS infrastructure went offline permanently, your deployment would continue to operate — inference, Ghost Weights training, model updates, all of it. That is what air-gapped means.
Ready to see this in your environment?
30-minute technical walkthrough. On-prem deployment. No pitch deck.
We'll walk through the air-gapped deployment architecture with your security and infrastructure teams.