Explainable edge AI: fusion and classification you can audit.
Sensor fusion · explainable classification · local processing.
CORE defines the software architecture for explainable classification and sensor fusion. Its purpose is to give the operator a shared track and the evidence behind it, with local processing as a design principle.
AI illustration · product concept
Fusion and classification, explained.
CORE is intended to fuse compatible sensor feeds and expose the basis for each classification. Physical integrations and classification performance require validation.
- Explainable classification.
- Multi-sensor fusion.
- Designed to run locally, at the edge.
- Auditable outputs.
- Source-agnostic ingest.
In the decision loop.
Multi-sensor detection: radar, passive RF and EO/IR — partner-supplied sensors integrated by VAERN, or the sensors already on site.
NEXUS is designed to link forward nodes into one network.
Explainable edge AI fuses sensors and classifies tracks.
The operator sees one air picture and authorises the response.
Capture nets, kinetic interceptors and RF jammers — supplied by authorised partners under their own authorisations and used only where the law allows. VAERN does not manufacture effectors.
Details.
| Function | Explainable classification & fusion |
| Compute | Local (edge) |
| Explainability | Auditable outputs |
| Inputs | Partner-supplied and third-party sensors — open interfaces planned, fitted to each integration |
| Specifications | Specified per deployment |
Figures are set per deployment — every site gets the mix it needs, and we publish no numbers we cannot stand behind.
AI classifies; it does not decide. Explainability keeps the human in the loop meaningful.