We provide innovative solutions to help businesses succeed.
01.
Object Segmentation
Algorithms we offer can be used for quick real-time segmentation of different objects and scenes in complex environments, making it ideal for a range of applications, including virtual reality, gaming, and AR.
02.
Object Detection & Tracking
Our object detection and recognition solutions are sure to pay off in such spheres as industrial safety, fashion analytics, cashier-less stores, road safety and lots more.
03.
3D Solutions
Our expertise in NeRF technique allows us to reconstruct 3D surface with great accuracy using only 100 images dataset.
04.
Pose Estimation
Our pose estimation, people tracking and detection solutions are built using state-of-the-art algorithms and deep learning techniques, making them highly accurate and reliable even in complex environments with multiple people and objects.
05.
Camera Calibration
We excel in camera calibration, including fisheye lenses, and have developed a custom calibration system that supports angles exceeding 180 degrees. Our expertise also extends to camera autocalibration solutions, which can adapt to changing environments without the need for manual calibration.
06.
Low-power Solutions
We use quantization to speed up the networks or enable the inference on devices that support fixed point computations.
Deploy everywhere
Cloud, local, or edge device deployment.
On edge devices
The data can be processed on the device used to capture it, thus saving big on server costs.
On-premise | servers
With on-premise inference the information stays at the source, which keeps your data secured and protected.
Cloud
Cloud solutions are sure to provide a flexible and cost-effective way to leverage computer vision models for a variety of applications. Easily scale up or down depending on the size and complexity of the case.
On edge devices
The data can be processed on the device used to capture it, thus enhancing user privacy and saving big on server costs.
Cloud
Cloud solutions are sure to provide a flexible and cost-effective way to leverage computer vision models for a variety of applications. Easily scale up or down depending on the size and complexity of the case.
On-premise servers
With on-premise inference the information stays at the source, which keeps your data secured and protected.
Gary Bradski, PhD is an internationally recognized entrepreneur and researcher in computer vision and machine learning. Gary founded and still runs OpenCV - the most popular open source computer vision library in the world.
Dr. Gary Bradski
Chief Scientist and President
Dr. Satya Mallick
Chief Executive Officer
Dr. Satya Mallick
Dr. Mallick is an accomplished computer vision and machine learning scientist and engineer. He has lead dozens of successful computer vision and machine learning consulting projects. His work has been featured in publications like the BBC, Time, Huffington Post, the Wall Street Journal, Oprah Magazine, and TechCrunch.
Anna Petrovicheva
Chief Technology Officer
Anna Petrovicheva
Anna is an expert in Computer Vision and Artificial Intelligence with a decade of successful experience in the industry. Working with teams worldwide on Deep Learning and Computer Vision algorithms and products, Anna’s created solutions for a wide range of industries, including medicine, security, sports, and virtual reality.
Grigory Serebryakov
Chief Development Officer
Grigory Serebryakov
Grigory is an expert in Computer Vision systems design and development, as well as their deployment to edge devices or cloud. He brings the powerful Deep Learning algorithms to the edge devices with the state-of-the-art compression techniques.
Tatiana Khanova
Chief Research Officer
Tatiana Khanova
Tatiana is a world-class specialist in Deep Learning and Computer Vision with more than 8 years of experience in both classic and Deep Learning algorithms for object detection (general and task-specific classes like vehicles, pedestrians), gesture recognition, image classification. Tatiana is an author of several papers at the biggest industrial conferences such as CVPR and ECCV.
Gary Bradski
Gary Bradski, PhD is an internationally recognized entrepreneur and researcher in computer vision and machine learning. Gary founded and still runs OpenCV - the most popular open source computer vision library in the world.
Dr. Gary Bradski
Chief Scientist and President
Dr. Satya Mallick
Chief Executive Officer
Dr. Satya Mallick
Dr. Mallick is an accomplished computer vision and machine learning scientist and engineer. Dr. Mallick is also the author of a popular computer vision blog LearnOpenCV.com. He has lead dozens of successful computer vision and machine learning consulting projects.
Anna Petrovicheva
Anna is an expert in Computer Vision and Artificial Intelligence with a decade of successful experience in the industry. Working with teams worldwide on Deep Learning and Computer Vision algorithms and products, Anna’s created solutions for a wide range of industries, including medicine, security, sports, and virtual reality.
Anna Petrovicheva
Chief Technology Officer
Grigorii Serebriakov
Chief Development Officer
Grigorii Serebriakov
Grigorii is an expert in Computer Vision systems design and development, as well as their deployment to edge devices or cloud. He brings the powerful Deep Learning algorithms to the edge devices with the state-of-the-art compression techniques.
Tatiana Khanova
Chief Research Officer
Tatiana Khanova
Tatiana is a world-class specialist in Deep Learning and Computer Vision with more than 8 years of experience in both classic and Deep Learning algorithms for object detection (general and task-specific classes like vehicles, pedestrians), gesture recognition, image classification. Tatiana is an author of several papers at the biggest industrial conferences such as CVPR and ECCV.
Get up to speed in the data science and machine learning domain, implementing analysis components in a distributed computing environment (e. Proficient with programming in Python & Scala, experience..
San Francisco, California, United States — All teams (roles across multiple teams)
