Industry
Defense & Critical Infrastructure
AI that runs where data cannot leave: zero-egress enclaves, self-hosted LLMs, and retrieval and vision inside closed networks you fully control, end to end.
If your mandate says the data cannot leave the network, most of the AI market disappears: every API-based tool, every cloud copilot, every vendor whose architecture assumes an internet connection. What remains is an engineering discipline, self-hosted models, retrieval over internal corpora, and vision, all running inside your perimeter, and that discipline is ours. For a security-sensitive organization, DataWise architected a zero-egress AI enclave end to end and built and demonstrated a fully isolated AI assistant inside the closed network. Nothing left the perimeter, and nothing needs to.
Is serious AI inside a closed network actually a solved problem?
Yes, at the highest security tiers in the world, and the evidence is first-party:
- Microsoft runs a fully disconnected GPT-4 environment for the US intelligence community, live for top-secret analysis, as reported by Bloomberg. Not a filtered gateway: air-gapped.
- France’s armed forces adopted sovereign, self-hosted GenAI through the Mistral framework, deployed on national infrastructure the state controls, per press reporting.
- The US Army’s CamoGPT serves roughly 75,000 users (per US Army reporting) on government-hosted infrastructure handling controlled unclassified and some classified information, with thousands reportedly using it daily as covered by DefenseScoop.
The pattern across all three: sovereignty has become a procurement requirement, not a preference. The question your organization faces is no longer whether AI can run disconnected, but who can engineer it at your scale and constraints.
What has DataWise actually built under these constraints?
Two layers, described here in deliberately abstracted terms because that is how this domain works.
- The architecture. For a security-sensitive organization we designed and architected a zero-egress AI enclave: private networking end to end, an MLOps foundation for deploying and updating models with no external connectivity, self-hosted LLMs, and vision object-detection.
- The working proof. We built and demonstrated a fully isolated, self-contained AI assistant inside the closed network: chat with organizational knowledge, retrieval over internal documents, deep-research workflows, and document and diagram generation. Everything ran inside the perimeter; nothing left it. In this domain paper architectures are cheap; a working assistant your people can interrogate is what earns the decision.
- Adjacent capability, same discipline. We built and demonstrated a Segment-Anything-based system that automatically segments airfields in satellite and aerial imagery and measures them with precision tooling, wrapped in a purpose-built UI with manual correction and report generation. Geospatial vision with a human in control, built to run wherever the imagery must stay.
We name no organizations, publish no environment details, and present each item at its exact verb tier: architected means architected, built and demonstrated means built and demonstrated.
Our security team will veto anything that phones home. How do you work?
By assuming the veto from day one. The architecture starts from zero egress: no external API calls, no telemetry, no models updating themselves over the internet. Model artifacts enter through your approved transfer process; versioning and monitoring live inside the enclave. And DataWise does not need access to your classified data: architecture and build proceed against your infrastructure constraints and representative unclassified data, and your cleared personnel operate the system on the real corpus.
We are critical infrastructure, not defense. Does this apply?
Directly. Water, energy, transport, and health operators carry regulatory and contractual constraints that put them in the same architecture class, and AI is already producing audited results in this world: the town of North Kingstown, Rhode Island documented 50 leaks found via satellite-based AI leak detection, $204,807 per year in savings and a 10.4-month payback, in its own municipal report. The same rule applies: the system must run where your data and your liability live.
FAQ
Are self-hosted models good enough compared to frontier cloud models?
Not at everything, and we will not pretend otherwise. Open-weight models are strong for retrieval-grounded assistants, internal research, and vision tasks. Characterization tests your actual use cases against candidate models, on your constraints, before any commitment.
How do models and software get updated with no connectivity?
Through the MLOps foundation designed into the enclave: controlled import through your approved transfer process, versioned deployment, and monitoring inside the perimeter. Update discipline is part of the architecture, not an afterthought.