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<pclass="text-sm text-subtle">On-device model adaptation in minutes — no reach-back to CONUS, no contractor, no weeks-long retraining pipeline.</p>
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<spanclass="tag">Emerging Capability</span>
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<h2class="text-2xl font-semibold text-[color:var(--white)]">Edge AI Retraining — Closing the Last Mile</h2>
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<pclass="text-subtle max-w-3xl">Every defense AI program hits the same wall: models trained in controlled environments degrade in the field. Different terrain, lighting, concealment methods, and threat profiles mean laboratory accuracy rarely survives first contact with reality.</p>
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<pclass="text-subtle max-w-3xl">The current DoD solution — ship data back to CONUS, retrain in a SCIF, push updates weeks later — doesn’t work for small teams operating in denied or disconnected environments. We’re building systems that retrain on-device, in minutes, directed by the operator who knows the environment best.</p>
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<h3class="text-lg font-semibold text-[color:var(--white)]">Why This Matters</h3>
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<liclass="flex gap-3"><spanclass="text-[color:var(--ceradon-blue)]">▹</span> SOF teams need systems that adapt to the environment they’re in <em>right now</em> — no reach-back to data centers.</li>
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<liclass="flex gap-3"><spanclass="text-[color:var(--ceradon-blue)]">▹</span> Border and perimeter security faces constantly changing threat profiles that demand rapid model updates.</li>
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<liclass="flex gap-3"><spanclass="text-[color:var(--ceradon-blue)]">▹</span> Partner forces can train on <em>their</em> environment with <em>their</em> data — no classified training sets required. A significant FMS enabler.</li>
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<liclass="flex gap-3"><spanclass="text-[color:var(--ceradon-blue)]">▹</span> Persistent surveillance at FOBs and COPs where the threat signature changes weekly.</li>
<liclass="flex gap-3"><spanclass="text-[color:var(--ceradon-blue)]">▹</span><strong>Operator-in-the-loop:</strong> Human oversight is the safety mechanism — operators validate retraining results before deployment.</li>
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<liclass="flex gap-3"><spanclass="text-[color:var(--ceradon-blue)]">▹</span><strong>Incremental learning:</strong> Adapt models with small, field-collected datasets without catastrophic forgetting of base capabilities.</li>
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<liclass="flex gap-3"><spanclass="text-[color:var(--ceradon-blue)]">▹</span><strong>Edge-native compute:</strong> Full retraining pipeline runs on embedded hardware — no cloud dependency, no exfiltration risk.</li>
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<liclass="flex gap-3"><spanclass="text-[color:var(--ceradon-blue)]">▹</span><strong>Closed-loop integration:</strong> Retrained models feed directly into autonomous response and C2 systems.</li>
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