Sibling Dynamics | The human side of AI, robotics, and security

The problem

Most AI runs somewhere you cannot see. Prompts, proprietary data, and model weights leave your network for a vendor’s cloud, and your assurance that they were handled correctly is a contract, not a control. For a research lab protecting unpublished work, a defense program under ITAR, or a life-sciences team under 21 CFR Part 11, that is the difference between a policy you can enforce and one you can only hope holds.

The usual answers do not close the gap. A cloud “private” tier still runs on infrastructure you do not own. A signed data-processing agreement is a promise, not a boundary. A security review of a black-box API can only test what the vendor lets you see. When being wrong means an export violation, a lost trade secret, or a data-integrity finding, the honest control is physical: the compute sits inside your perimeter, and the data never crosses it.

What we do

We deploy and operate AI compute that you own outright. We install NVIDIA DGX-class hardware inside your facility, harden it to your security standard, connect it to your governed model stack, and keep it running. On-premises for controlled environments, fully air-gapped for the ones that require it.

The result is a closed loop. Inference happens on your silicon, on your network, under your access controls. Weights and data stay on disks you hold. When your regulator, your auditor, or your own security team asks where the AI runs and what leaves the building, the answer is nothing, and you can show them the rack.

We run this model ourselves. Our own governed systems operate on sovereign hardware every day. The deployment we build for you is the one we already trust with our own work.

How we do it

Scope and threat model first. We start from your data-classification and residency requirements, your regulatory frameworks, and your threat model, then specify hardware to match. Air-gapped or network-connected is a decision we make with you on the evidence.
Install and harden. We provision the hardware on site, apply a documented hardening baseline, and validate the build. You get the configuration record, not just a working box.
Integrate the governed stack. We connect the compute to your models and to the admissibility controls and audit trails from our governance work, so the system does not just run privately, it runs accountably.
Manage it, or hand it over. We operate the environment under a managed support plan, or document it and train your team to run it. You choose the level of dependence; we do not manufacture lock-in.

What you get

Deployed, owned GPU infrastructure inside your facility, on-premises or air-gapped.
A documented hardening baseline and the as-built configuration record.
Integration with your governed model stack, admissibility controls, and audit logging.
An operations runbook covering patching, monitoring, backup, and recovery.
A managed support plan with a defined response standard, or a documented handover to your team.
A residency evidence pack: a plain account of where the AI runs and what does and does not leave your control, written for an auditor.

Frameworks and compliance

Sovereign compute is a control, not a certificate. It maps to ISO/IEC 27001 for the control baseline we harden to, ITAR and export-control obligations, 21 CFR Part 11 and GxP for demonstrable system control and data integrity, NERC CIP for critical-system separation, and the EU AI Act and NIST AI Risk Management Framework, where knowing exactly where a system runs is foundational to governing it.

Who it’s for

Research labs protecting unpublished data and pre-patent work. Defense and aerospace programs under ITAR or handling controlled technical data. Life-sciences organizations under 21 CFR Part 11. Energy and utility operators separating critical systems from external networks. Any security-sensitive organization for which “the data left our network” is not an acceptable sentence.