AI-native trust runtime for autonomous fleets in degraded conditions.
We turn observations into signed evidence and reconcile fragmented mesh state after blackout.
The runtime stack: foundation carries everything above it. Column thickness encodes load.
Trust, mesh, and AI compile into a single decision substrate. No layer ships alone.
Each observation becomes signed evidence.
Each node keeps a local record through blackout.
The runtime reconciles state when links return.
Built by OEMs. Operated in real environments. Already part of the fleet.
Four software layers.
One deployable trust package for autonomous systems.
Turns onboard sensor observations into signed evidence objects.
Supports local navigation when GNSS becomes unreliable.
Maintains trusted network state through isolation, partition and rejoin.
Shows operators what the network believes, why it believes it, and what was rejected.
EINHARD keeps local evidence active through blackout and reconciles network state when links return.
EINHARD does not manufacture airframes or sensors.
The trust runtime integrates with existing AI autonomy stacks through standard interfaces — PX4, MAVLink, ROS 2.
Delivered as SDK, integration package, or retrofit kit.
Validates autonomous system operations through replayable trust, provenance, and assurance reports. For fleets already deployed and operating — logistics, infrastructure, industrial autonomy.
Full integrated trust runtime across all four layers — signed observations, mesh reconciliation, trust-aware decisions, replayable evidence. Mesh-native, platform-agnostic.
Patent applications across the Evidence Mesh protocol, GNSS-denied navigation, and mesh reconciliation.
EU-sovereign IP. Filed with the DPMA in Berlin.
Engineered in Berlin under EU jurisdiction. EU-sovereign IP. Built for European autonomy programs and industry partnerships across logistics, infrastructure, and industrial autonomy.
We work with autonomous systems operators, drone OEMs, infrastructure operators, and research consortia across Europe.