Use Cases
white paper
AI that’s built to survive first contact
Most edge AI fails not because the model was wrong, but because the system was never designed to adapt. A new theater. A changing adversary. A different environment. Each one exposes assumptions that were never tested.
This white paper draws on hundreds of thousands of device hours across U.S. Army and Navy deployments — including Project AMMO, where Latent AI reduced model update times by 33x — to define four engineering standards for edge AI that holds up when conditions don’t.
dod ai procurement
The questions every program should be asking
The DoD has made 30-day AI model deployment a procurement criterion. Programs are moving. But speed without survivability creates a different problem.
Can the system be retrained in the field without an ML engineer?
Most edge AI requires a data scientist and a lab environment to update. If your system can’t adapt at the point of deployment, it can’t adapt when it matters.
Does it operate fully disconnected from cloud infrastructure?
DDIL environments aren’t edge cases — they’re the operational baseline. Cloud-dependent AI isn’t edge AI.
Can it update securely over the air in a degraded environment?
OTA updates sound simple. Doing them securely, reliably, and without connectivity is an engineering problem most platforms haven’t solved.
Has the vendor actually deployed this at scale, or demonstrated it in a lab?
Benchmarks are not deployments. Ask for device hours, program names, and field validation events — not demo footage.
white paper
AI Under Fire: Designing Edge AI Systems to Survive Mission Reality
This white paper answers those questions from the field, drawn from programs like the U.S. Navy’s Project AMMO, where Latent AI reduced model update times by 33x, and hundreds of thousands of device hours across Army and Navy deployments.
Deployed in U.S. Army and Navy programs operating at the tactical edge.
Resources
More from the field
