Distributed Human To Machine Learning
The Operator Doesn’t Need Another Feed. They Need a Teammate.
Anyone with time at the tactical edge knows it is not the technology that fails first. It is the assumptions underneath it. Comms drop. Sensors glitch. People go silent because they are busy fighting. That is not the surprising part.
The real issue is how your system behaves when it happens. Most do not behave well. The core problem is not that AI cannot process fast enough or detect enough. It is that it does not understand the rhythm of the mission. It does not grasp when the operator’s silence means stay out of the way, or when an override is not a rejection but a pivot. We have built models to detect and react, but not to function as part of a team under pressure.
AI AUTONOMY
EMBEDDED EDGE AI
/ THE PROBLEM /
AI Blackboxes Have Over-Engineered for the Demo and Under-Built for War
/ OUR SOLUTIONS /
What We Build: AI That Knows Its Role
System Awareness
Model Limits
Mission-Centric Design
/ TECHNICAL DEEPDIVE /
How the Our Models Works in Practice
Autonomy That Adjusts Based on Mission Context
We do not rely on hardcoded autonomy levels. Instead, each system uses behavior trees layered with simple probabilistic checks to adjust how assertively it acts. For example, if a UGV normally requests clearance before entering a structure but detects clear passage, it may act without prompting unless the mission phase indicates caution.
Operators can manually override or shift modes using tactile or simplified UI inputs. The system also reacts to the absence of input. If the team is under fire and not responding, the AI lowers its output rate and shifts to monitoring mode. The result is an AI that adjusts to tempo without requiring constant tuning.
Decentralized Coordination Built for Comms Constraints
Our agents maintain local tactical models. These are belief-weighted graphs that track what the agent sees and how it interprets threats or terrain. When agents are in contact, they share only compacted updates with specific nodes based on relevance and authority.
This avoids flooding the network with unnecessary data. For instance, a ground robot that spots something unusual does not broadcast to everyone. It pushes the update to the drone that has coverage to verify. If the comms drop, the local model continues operating with what it knows. This coordination model is built around tolerance for partial views and local decision-making.
Fusion That Highlights Change, Not Nois
The system combines multiple inputs, such as visual, thermal, and audio, on device. Rather than pushing every classified object, it looks for inconsistency between modalities. If most inputs suggest a benign environment, but one indicates movement or a heat bloom that does not align, that becomes the priority.
This change-based fusion ensures that operator attention is drawn to anomalies, not routine confirmations. The fusion model is lightweight and structured to run on field-deployable hardware without relying on cloud compute or high-bandwidth backhaul.
Post-Mission Adaptation Based on Operator Behavior
Currently, we do not support in-mission model updates. But we do support systems that log operator overrides, alert dismissals, and other feedback markers. This data is reviewed post-mission to refine future model tuning.
Adaptation occurs between operations, not during them. This maintains trust and system integrity while allowing the AI to improve over time. Teams can opt in to review or ignore updates based on mission needs.