AI mission orchestration platform

Mission Fabric™

Coordinate mission planning, sensing, data fusion, edge intelligence, and autonomous assets inside one secure operational layer.

Edge Cloud, hybrid, and field-deployed decision support
1s Live tracking patterns for moving assets and manifests
SoI Policy-aware access, provenance, and audit controls
Plan Shape workflows around mission context and changing conditions.
Sense Bring sensor, platform, environmental, and asset data together.
Fuse Resolve fragmented feeds into a common operational picture.
Adapt Coordinate humans, AI agents, and autonomous systems in motion.
Mission layer

From edge to orbit to ground, operations become composable.

XRDNA positions Mission Fabric as a software-defined orchestration layer for mission data, AI models, autonomous assets, and operational workflows. The result is a planning and execution environment designed for distributed, high-tempo operations.

XRDNA Mission Fabric architecture connecting edge devices, ground stations, space systems, air systems, ground systems, and mission operators
Core capabilities

Built for contested, distributed, and high-consequence environments.

The platform story centers on decision advantage: faster planning, clearer data relationships, resilient access control, and coordination across mixed human-machine teams.

AI-enabled planning

Use operational context to plan, simulate, and adjust mission workflows before conditions shift.

Real-time data fusion

Unify sensor, platform, environmental, and operational inputs into one mission picture.

Edge-to-cloud intelligence

Deploy decision support across cloud, hybrid, and edge environments where operations demand it.

Autonomous coordination

Support unmanned, autonomous, and human-operated systems in shared mission flows.

Predictive analytics

Anticipate constraints, risks, opportunities, and likely outcomes before they shape the mission.

Interoperable architecture

Connect existing systems, data sources, and workflows through a modular software layer.

Operational mesh

Manifests bind People, Places, Things, and policy.

Mission Fabric uses manifests as living containers for routes, assets, identity, access rules, and mission state. They move with the operation instead of staying trapped in static systems.

People with identity and mission permissions Places such as depots, launch sites, zones, and stations Things with telemetry, position, battery, signal, and status
Explore manifests
Logistics command center Active

Supply Chain Alpha

eva://us.logistics/manifest/sc-alpha-2024

LA Warehouse complete Phoenix Hub complete Denver Distribution current Chicago Terminal pending
Mission journeys

One fabric for logistics, space, defense, and AI workflows.

Mission Fabric examples span manifest-driven supply chains, orbital deployment flows, multi-domain operations, and agent-assisted optimization.

Supply Chain Manifest
manifest: "Supply Chain Alpha"
evaAddress: eva://us.logistics/manifest/sc-alpha
status: active
currentStop: 3 of 7

assets:
 - person: Driver-142
 - thing: Truck-FL-8823
 - thing: Cargo-RFID-9921

controls:
 - live position polling
 - route change approval
 - SoI Social policy
Safety and governance

SoI-aware control for location, time, approval, and audit.

The security model described by XRDNA combines Sphere of Influence policies, geofenced boundaries, timeline-based permissions, approval flows, and observability.

Policy inheritance

Linked assets can inherit visibility and access rules from the mission or manifest they join.

Location security

Coordinate-based controls support geofences, zone entry events, and route deviation awareness.

Timeline enforcement

Permissions can follow mission phases, stop windows, and completion state.

Audit trail

Journey history, asset interactions, and policy decisions remain observable for compliance.

Deploy Mission Fabric

Ready for operational integration.

Bring Mission Fabric into defense, aerospace, logistics, industrial, or autonomous-system environments with help from XRDNA's sales and engineering team.