What We Do

We provide real-time AI solutions for extracting actionable information from sensor data. Using our intelligent multi-sensor analytics platform, customers can configure and deploy real-time processing workflows at the edge. By directly connecting AI to the physical world, our customers realize greater levels of automation, understanding, and efficiency.

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Products & Solutions

kvSonata

Real-Time Video AI and Analytics

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kvPrelude

Real-Time Radio Frequency (RF) Analysis

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Announcements & News

Join the KickView team at GTC 2018

Join the KickView team at GTC 2018 to hear about real-time AI systems. Our CEO, David Ohm, PhD will be presenting. The topic will be Deep Learning For Intelligent Multi-Sensor [...]

Real-Time Object Detection and Tracking Presentation

Join us at the Colorado Data Science Meetup where KickView research engineer Tevis Gehr will give a survey of some challenges and applications of machine learning to real-time object detection and [...]

Intelligent Multi-Sensor Analytics (IMSA) For Security Systems

KickView is working with the Air Force Research Laboratory (AFRL) to develop capabilities for the next generation security systems deployed at Air Force facilities and airfields around the world. The [...]

KickView to present at Denver AI Meetup

KickView will be presenting at the NVIDIA sponsored Denver AI Meetup on March 21st. Exploring the World of Intelligent Multi-Sensor Analytics (IMSA) w/ Kickview Wednesday, Mar 21, 2018, 6:00 PM [...]

Working and Playing in Colorado

At KickView we feel fortunate to be living and working in beautiful Colorado. There are many outdoor activities to enjoy and the mountains provide close access to a variety of [...]

Deep Learning Research with AFRL

KickView conducted research in multi-sensor analytics with deep neural networks as part of the Small Business Technology Transfer Research (STTR) program with the Air Force Research Laboratory (AFRL). The effort [...]

Deep Learning and Signals

KickView developed the popular online lab for NVIDIA on using deep learning for RF signal processing. Specifically, CNNs can be utilized for low-SNR signal detection problems. We used a custom [...]