DOE and NRC discipline applied to energy AI.
The principal's prior federal delivery includes the Nuclear Regulatory Commission and the Department of Energy. The operational discipline of high-consequence, air-gapped environments translates directly to commercial energy AI work: operating against a determined adversary, in an environment where the cost of being wrong is higher than the cost of being slow.
The operational catalog for energy.
- Governance layer around vendor autonomous control agents. Autonomy tiers, kill switches, audit logs, and human-review queues built into the existing process safety case.
- AI red-team and adversarial testing against energy-sector AI deployments. The threat model for an upstream agent is not the threat model for a retail chatbot.
- Multi-tenant isolation engineering for sovereign energy operators running shared AI infrastructure.
- Control implementation against the NIST AI Risk Management Framework, NIST 800-53, and sector-specific frameworks. We engineer to the framework. The audit is performed by somebody else.
For operational data that cannot leave the perimeter.
Public LLM APIs are not an option for production telemetry, control-system data, or operationally sensitive material. BeitSystems deploys open-weight models on the operator's infrastructure: on-premise, inside the operator's cloud account, or in an air-gapped enclave adjacent to the OT network. The work spans inference engine selection, GPU resource management, retrieval and grounding pipelines, observability, and the security perimeter around the deployment.
The honest constraint.
We do not design control loops. We do not write the physics of process safety. We do not build the reservoir model or the grid stability simulation. Those are the work of vendors and operators who have spent decades on them. We work above the control system and beneath the regulator, in the band where AI governance, security, and audit live.