The perception layer, explained.
One system that takes models from general-purpose to mission-grade — and keeps them there.
Perception in. Decisions out. Everywhere your analysts and agents already work.
Structured perception outputs surface inside GXP Xplorer, SOCET GXP, and GXP Fusion as first-class observations — typed, attributed, and ready to brief.
Closing the long-tail gap.
The Perception Engine fine-tunes attention onto the discriminating features that determine real classification — hull configuration, deck layout, equipment clustering, mast profile. Value compounds alongside the operational data you already have, never against it.
- Discriminating features over surface appearance.
- Mission-relevant taxonomy, not catalog taxonomy.
- Grows with your data — not in competition with it.
Stress-test perception before the mission does.
Structured, repeatable benchmarks scored against the rare targets and failure modes that matter — off-nadir geometry, haze, occlusion, unusual configurations. Pass/fail is measured against operational criteria, not generic accuracy.
"18–24 months and $1M per detector is unacceptable. The status quo cannot keep pace with the threat."
Persistent perception at the action threshold.
Persistent object understanding with confidence calibrated at the action threshold. Temporal context drives change detection across revisits. When an agent fails, the perception layer is diagnosed and retrained — a closed loop, not a fire-and-forget delivery.
- Persistent identity across revisits and sensors.
- Confidence calibrated to operational thresholds.
- Failure → diagnosis → retrain. Closed loop.
Ready for the environments that matter.
Isolated VPCs, encrypted at rest. Classified-network ready.
Outputs available in GXP Xplorer, SOCET GXP, and GXP Fusion — the systems IC and DoD analysts use daily.
Routed into exploitation environments already in production use.
Containerized for disconnected and forward-deployed operations.
Model-agnostic by design. Programs running any foundation model stack consume AgileView perception outputs equally.
Run AgileView against your own models.
Request a sample dataset on a target class of your choosing and benchmark it against your current detection stack.