Built for the last watt.

Edge AI compute for contested and denied environments.

AugSense solves the edge computing trilemma: high-fidelity AI inference under extreme limits on size, weight, power, and connectivity (SWaP-C), without giving up accuracy or latency.

01 / 03

Multi-Modal Sensor Fusion

Every sensor sees part of the picture. EO cameras lose the scene at night and in smoke. IR sees heat but not identity. RF catches emitters a camera never will, and acoustic picks up what's behind the ridge.

Our fusion engine ingests these modalities and correlates them into one operational picture, on the platform, in real time. That means fewer false alarms, tracks that survive when one sensor drops out, and a single output an operator or C2 system can act on. Fusion runs alongside detection on the same edge compute, so adding a sensor adds information, not another stream to send home. It started in BeAST, fusing vitals, motion, and environmental data on an operator's ear. It now runs on unmanned systems.

Related service: Model Build & Deploy →

02 / 03

Semantic Compression

In a contested environment, bandwidth is the first casualty. A single HD video feed can fill a tactical link, and when the link degrades, raw video is the first thing to break.

Semantic compression changes what gets sent. Instead of pixels, the edge model transmits what matters: detections, tracks, classifications, and their context, with keyframes only when they're needed. Mission-critical intelligence gets through links too thin for video, and many feeds fit in the bandwidth one stream used to take. We measure it honestly: bandwidth saved against detection accuracy on real ISR streams, so you know exactly what you trade for every bit you don't send.

Left: raw video over a degraded link. Right: the same scene, sent as meaning, not pixels.

03 / 03

Neuromorphic Processing

Always-on sensing is a power problem. A GPU that watches continuously drains a battery in hours, so most systems either sleep and miss events or burn power they don't have.

Neuromorphic processing takes a different approach. Spiking neural networks are event-driven: they compute only when something in the input changes, so idle power falls to a fraction of a conventional GPU's. That makes always-on detection practical on small platforms, wearables, and remote sensors running on batteries or solar. We port detection models to spiking architectures, run them on neuromorphic boards alongside conventional AI accelerators where that makes sense, and measure the power floor directly, so you see what continuous inference really costs.

Integration

Platform-agnostic

Hardware-independent by design. Our stack runs on Jetson-class modules and other off-the-shelf edge compute, with AI accelerators or neuromorphic boards where the power budget calls for them. It integrates through TAK, ROS, or your own APIs, so it fits the platforms, ground stations, and C2 systems you already run.

We're a software layer. You keep your airframe, your sensors, and your supply chain.

Jetson-class modules
AI accelerators
Neuromorphic boards
TAK
ROS
Custom APIs

Have a platform, a dataset, and a problem?

Tell us the hardware, the sensors, and the constraint. We'll come back within a week with a scoped proposal.

Book a scoping call