Reality AI is especially good at identifying surface textures and discontinuities in surface texture across all imaging types.

RGB, LiDAR, radar, thermal, multispectral and hyperspectral imagery are all in increasing use for a variety of tasks across industries.

The examples below show Reality AI applied to infrastructure inspection, rust and corrosion detection, damage detection, terrain analysis, and automotive uses.

Customer Use Case:
North American Lighting (a division of Koito Group)

In this video, Takeshi Masuda and Amit Mehta, of the North American Lighting Silicon Valley Lab, a new division of the Koito Group, explain how this tier one automotive supplier is using Reality AI technology to improve the accuracy of the Adaptive Driving Beam (ADB).

Koito is the world's leading maker of exterior automotive lighting, and they announced the concept for their next generation ADB headlight, featuring Reality AI technology, at the 2018 Consumer Electronics Show.

Learn more with our Technical Whitepaper

Explore the technical details behind the Reality AI approach to machine learning with signals

  • Why signals require a different approach than other machine learning problems

  • The importance of "features" to effective machine learning

  • Why the FFT probably isn't good enough, and what other options are better

  • The difference between Reality AI and Deep Learning