Tactical-edge AI means running models on hardware that the warfighter actually carries, drives, or deploys — not a server rack in a CONUS data center. Zapata AI designs systems that produce useful inference where bandwidth is scarce or nonexistent, power is budgeted, and the operator cannot wait for a cloud round-trip.
The operational problem
Most commercial AI stacks assume reliable reachback. At the tactical edge, assumptions break: RF windows close, link budgets drop, and operators hit encrypt/decrypt and cross-domain overheads that commercial workloads never see. Moving inference into the vehicle, the pack, or the airframe is the only way to keep the decision loop inside the operator’s tempo.
How we build for the edge
- Nvidia Jetson-class hardware for body-worn and vehicle-mounted deployments; Nvidia DGX-form-factor nodes for brigade- and battalion-level aggregation.
- Quantized and distilled models sized to the actual thermal and power envelope of the platform — not a 70B general-purpose model shoehorned onto a 60W budget.
- Pre-accredited software supply chain so a mission-specific payload can be signed, pushed, and loaded without a full re-ATO cycle.
- Graceful degradation: the platform stays useful when links drop, syncs when they’re restored, and is explicit about what data is stale.
Where this shows up in ANIMAS
Tactical-edge AI is the foundational use case for our ANIMAS platform. ANIMAS packages mission-specific microservices so operators can reconfigure the device in the field without reachback and without maintainer assistance.
To discuss a capability brief, contact the Augusta team.