We train models to reason about tasks, resources, and constraints using simulated missions.
Our planning models are trained using reinforcement learning within realistic constraints such as ISR gaps, platform failure, shifting goals, and degraded communications. The model learns to build task graphs, allocate resources, and manage dependencies.

Models are trained offline. Once deployed, they execute fixed policies using only local observations. Every decision path is traceable. This is trained behavior, bounded by mission logic, and reviewable by operators.

Plans adjust in real time through structured graph updates.
When a task becomes invalid, the model recomputes a viable path from its current state. This includes resolving task conflicts, checking timing windows, and reassessing resource availability.

Search space is limited to valid fallback options defined in advance. Replanning occurs quickly based on mission logic and preconfigured alternatives.

Units execute independently while maintaining shared mission context.
Each unit carries its own mission context and tasking model. There is no reliance on a central coordinator. Platforms share updates when communication is available, but full synchronization is not required.

If a unit loses comms, it continues executing its last known valid plan and re-syncs when connectivity returns. This supports continued operation during partial network failure.

Models are designed to run on embedded systems with strict compute and power budgets.
Planning models are deployed on ARM-class processors and neural accelerators. They are optimized for deterministic latency.

Inference runs locally using data from onboard sensors such as GPS, IMU, telemetry, and camera feeds. Feature extraction is tailored to decision needs, avoiding unnecessary compute overhead.

There is no dependency on cloud APIs or remote model updates. Everything required is stored and processed locally.

Human operators define the mission. AI supports planning and execution within that intent.
Operators provide goals, constraints, and fallback rules using structured inputs. These are compiled into a policy space that the model uses.

As missions evolve, the operator sees proposed changes with clear justifications. Operators can accept changes, modify tasks, or halt replanning entirely. The human stays in control of the plan.

Fallbacks and task substitutions help sustain operations when plans fail.
Each plan includes predefined alternatives. If a task becomes impossible, the system switches to the next valid option and logs the change.

This logic does not invent new goals. It works to complete what is still achievable, reducing mission aborts and operator workload during degraded conditions.