Rovis.AI is our Artificial Intelligence platform used for designing, training and deployment of Deep Neural Networks (DNNs). Our approach to AI is composed of a dual software systems:

Rovis.AI.workbench, which is a Python based software package used to design DNN architectures and train them on saved blockchains; it uses PyTorch, TensorFlow and ONNX (Open Neural Network Exchange) as low-level AI libraries.

Rovis.AI.inference, representing our C/C++ based optimized inference engine for trained DNNs running in real-time within the blockchain.

The focus of our DNNs is on real-time inference for computer vision and perception. To achieve real-time capabilities, we use multi-tasking deep learning architectures which are sharing common backbones, while inference results are provided via independent network heads (e.g. object detection, scene segmentation, 3D reconstruction, features tracking).


Simultaneous semantic segmentation, object detection and keypoints tracking using RovisLab's Multi-tasking Deep Neural Network architecture.

Driving scene perception and 3D reconstruction of the road model on a highway.