Instead of needing a pilot for every drone, one person tells the AI what the mission is and the swarm figures out how to do it together.

Seeing Systems' Aerie platform uses agentic AI to transform drone swarm operations from a many-operators-to-many-drones model into a single-operator-to-many-drones paradigm. The system ingests mission objectives, environmental data, and real-time sensor feeds to autonomously allocate tasks across the swarm—assigning reconnaissance roles, strike priorities, and fallback behaviors without requiring granular human input for each drone. The AI agents negotiate roles among themselves, dynamically re-plan when drones are lost or threats emerge, and surface only critical decision points to the human operator. This dramatically reduces cognitive load, training requirements, and the personnel footprint needed for complex multi-drone operations, which is essential for resource-constrained military units operating in denied or degraded communications environments.

It's like being a restaurant manager who just says "busy Friday night, 200 covers" and the entire kitchen staff self-organizes who's on grill, who's on desserts, and who covers when someone burns their hand.

The drone teaches itself to dodge electronic jamming and enemy fire by learning from what happened to drones before it on the same battlefield.

Seeing Systems' forward-deployed engineering model in Ukraine creates a uniquely powerful feedback loop: drones encounter real electronic warfare (EW) jamming, kinetic threats, and environmental challenges, and that operational data is fed back into ML models that continuously improve autonomous flight behaviors. The system likely uses imitation learning from skilled FPV pilots combined with reinforcement learning in simulation to develop robust navigation policies that can adapt to GPS denial, RF jamming, and visual obscurants. Each deployment generates training data that makes the next generation of autonomy more resilient. This is a decisive advantage over competitors relying solely on simulation or controlled test environments—Seeing Systems' models are trained on the ground truth of modern electronic and kinetic warfare, producing drones that can autonomously navigate to targets even when communications are degraded or severed.

It's like a delivery driver who doesn't just use Google Maps but actually remembers every pothole, speed trap, and road closure from thousands of previous drivers' dashcam footage—and reroutes before you even hit the problem.

The drone's onboard AI recognizes what it's looking at—vehicle, structure, decoy—and calculates the best angle of attack all by itself, even if it loses contact with the operator.

The Bandit drone platform integrates onboard computer vision models that perform real-time target detection, classification, and prioritization without requiring continuous operator input. Using lightweight convolutional neural networks optimized for edge deployment, the system can distinguish between military vehicles, structures, personnel, and decoys under varying lighting, weather, and occlusion conditions. Once a target is classified, the AI computes an optimal terminal approach vector—factoring in target geometry, armor profile, warhead type, and wind conditions—to maximize strike effectiveness. This capability is critical for one-way effector missions where communication links may be severed during the terminal phase. The modular hardware architecture of Bandit allows rapid swapping of vision modules and ML accelerators as new, more capable chips become available, ensuring the platform stays ahead of adversary countermeasures like visual camouflage and IR decoys.

It's like a smart bowling ball that not only rolls itself down the lane but picks which pin to hit and adjusts its spin mid-roll for a guaranteed strike—even after you've already let go.