Different signals.
Common ground.
A modular architecture for the work that makes physical intelligence useful: time synchronization, spatial alignment, local inference, and explainable outputs.
Talk architectureA shared sense of time
Align timestamps and sampling rates so complementary observations can be interpreted together.
A common spatial frame
Model sensor relationships through calibration and coordinate transforms before combining their observations.
Fusion at the right layer
Consider data, feature, and decision level fusion methods according to the workload and available signals.
Compute near the source
Match local perception models and inference runtimes to the target edge hardware and operational requirements.
Events with context
Publish observations with the source, timing, and pipeline context that downstream applications need.
Visibility across deployment
Plan for device health, model versions, event logs, selected recordings, and replay or diagnostic workflows.
Choose what moves upstream.
Not every raw frame or measurement needs to travel to a central service. The HoloNexa approach puts time sensitive processing near sensors and shares selected events and metadata where useful.
A complementary control plane can provide deployment visibility without making every local decision dependent on a cloud round trip.
Explore integration patternsMake every sensor part
of a smarter system.
Bring your sensor stack. Let’s talk about what it could see.