Real-World Intelligence Platform
The intelligence layer
for the physical world.
Dragonfly turns vehicle-mounted cameras, autonomous drones, ground robots, and fixed sensors into a single coordinated network - and the intelligence layer that runs them.
System Architecture
Three layers. Zero single points of failure.
Fixed infrastructure. Every vehicle, every sensor, already on the network.
Vehicle-mounted camera systems on existing fleet vehicles, fixed cameras at critical infrastructure points, and existing sensors of any manufacturer all feed the same intelligence layer. No rip-and-replace required. Vehicles in motion are still fixed nodes relative to the platform - they just move with the asset they observe.
- Vehicle-mounted multi-camera arrays with edge compute
- Fixed cameras and sensors from any manufacturer integrated
- GPS-tagged, timestamped imagery on every frame
- Existing infrastructure becomes an intelligent node
The mobile agent mesh. Coordinated drones, robots, and autonomous nodes.
Aerial drones, ground robots, and dock-based autonomous agents that deploy on demand and reposition dynamically in response to events. When a Layer 1 node flags an anomaly, the platform dispatches the right agent automatically - vegetation contact triggers an aerial inspection, a road defect triggers a ground sweep. The mesh is hardware-agnostic and grows as new agent types become viable.
- Autonomous aerial agents with dock-based deployment
- Ground robots for inspection and physical tasks
- Distributed mesh coordination across heterogeneous hardware
- Dispatched automatically by the intelligence layer
Unified intelligence. One operator, the complete picture.
The unified intelligence layer fuses inputs from every node type - vehicles, drones, robots, fixed sensors - into a single operational picture. Detections are correlated across nodes, anomalies are flagged automatically, and the operator sees what matters across the entire network without drowning in raw data.
- Cross-node anomaly detection and event correlation
- Automated dispatch across heterogeneous agents
- Single operator interface regardless of network size
- Edge compute - no cloud dependency for time-critical detection
Hardware Philosophy
Hardware-agnostic by design.
The hardware is the observation layer. The intelligence is the product. New node types plug in without rebuilding.
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Vehicle Cameras
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Autonomous Agents
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Edge Compute
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Connectivity
The Data Network
The intelligence compounds with every deployment.
Every node on the Dragonfly network contributes to a continuously growing dataset of physical infrastructure condition. The dataset makes detection sharper for every operator, surfaces issues on their routes before their own trucks get there, and trains the autonomous agents that come next.
01 SHARED LEARNING
Better detection across the network.
A defect pattern detected by one operator improves detection accuracy for every operator. Models that ship to one node ship to all of them.
02 EARLY WARNING
Issues on your routes, before your trucks reach them.
Your trucks don't cover every road every hour - but other fleets on the network do. When they detect a closure, a hazard, or a downed asset along your service area, your operations team sees it in time to reroute, reschedule, or respond.
03 AUTONOMY FOUNDATION
The training data for what comes next.
Infrastructure workflow data collected today is the foundation for the autonomous agents that handle the work tomorrow. You cannot automate what you have not mapped.
See it in your environment.
Tell us about your infrastructure and operations and we'll show you what the platform looks like for your environment.