Custom-trained machine learning algorithms for space applications

FeatherApps are custom trained machine learning algorithms for spaced-based applications. The algorithms are designed to function on-orbit, and run on AI computing hardware. 

The algorithms can be trained to support a range of applications, including: tracking a satellite docking feature using machine vision; detect and locate nearly space objects; perform object detection on Earth observation images in real-time; etc. 

FeatherApps are built based on the customer's requirements and the algorithms are trained using customer defined datasets created from real-data, synthetic data, or a combination of both. To ensure flexibility during mission operations, FeatherApps are capable of being configured and updated on-orbit to meet the changing mission needs of any customer.


Autonomous Rendezvous Proximity Operations and Docking

Use machine vision to track satellites on-orbit, enabling autonomous operations in space

Satellite Inspection and Servicing

Map, locate, and detect nearby objects in real-time to support autonomous control of satellite systems

Earth Observation Image Processing

Perform image classification and object detection onboard


Superior Performance

Models can run inferences on satellite images in ~3 milliseconds with 96% accuracy. 

The on-board machine vision models are capable of being configured and updated in orbit to meet the changing mission needs of any customer.

Mission Flexibility

Reduce Cost

Send only relevant information to the ground, reducing bandwidth and providing low-latency insight.