Deployable AI for the contested commons

Fielding models for the contested edge.

From a common operating picture to a common operating model.

Perception, state, and confidence that run on the compute the platform carries — turning degraded field data into C2-ready state.

Thesis
Common Operating Picture to Common Operating Model.

Seeing the battlespace is no longer enough. We build world models for defense autonomy — systems that model what is true, uncertain, changing, and actionable, not just what a sensor happened to capture.

The advantage compounds in the field. Every deployed node feeds back mission traces, hard cases, and model-evaluation artifacts — a field-data loop that only accrues to whoever deploys first.

Capabilities

Models built for the field.

What it takes to get a model out of the lab and onto a platform that’s flying, jammed, and cut off from home.

On-platform

Runs on the hardware you field

Models sized to the SWaP, power, and latency the platform actually carries — not a datacenter it can’t reach.

Degraded by default

Holds up when signal doesn’t

Perception and state that keep working through GPS denial, comms loss, and disagreeing sensors — reasoning locally.

Evidence is product

Trusted enough to field

Every output carries confidence; every model ships with evaluation, drift, and failure-case evidence that survives scrutiny.

Field-data loop

Improves from deployment

Replay and hard-case mining feed real conditions back, so edge models get stronger with each mission.

Careers

Field models to the edge.

We’re hiring across edge perception, state estimation, and model evaluation. If you want your work measured against degraded, contested, adversarial reality — not a benchmark — talk to us.

Join us