Architecture and governance for an AI mission-planning platform.

A defense AI program had real capability. It could build a deployment scenario, simulate it against the real-world frictions that derail operations (weather, maintenance, contested conditions), and tell a planner whether a course of action was actually feasible — far faster than the manual process it replaced. But it had no defined architecture and no shared language across the people building and using it: engineering, data science, and military operators each described the system differently, integration was ad hoc, and operators couldn't fully trust or act on what it produced. It couldn't scale past prototype because no one agreed on how the pieces fit — or on how a human stayed in command of a system moving that fast. Engaged initially to bring order to the architecture, we ultimately led the platform's design, established its governance model, and built the engineering organization responsible for delivering and sustaining it.

• A coherent system architecture — the data layer, the simulation engine, and the operator-facing surfaces cleanly separated
• Decision frameworks that made the AI's reasoning legible and assessable to the operators responsible for the decision
• A shared data layer and explicit interfaces, allowing engineering, data science, and operators to describe — and integrate — the same system consistently
• A shared vocabulary, and the engineering organization needed to deliver, govern, and sustain it

The program moved from an early prototype to a production-scale platform deployed in government cloud environments — not by adding features or model horsepower, but by establishing the architecture, governance, shared language, and engineering organization that allowed the existing AI capability to scale while keeping human planners in command.