DeepScale

Overview

DeepScale AI is a computer vision startup focused on real time automotive perception systems that give cars self driving capabilities.  The team deploys onto cars pre-trained neural networks able to take information from any type of sensor system (including cameras, LiDar, etc.).  They are focused on "efficient" deep learning that is high powered but doesn't rely on expensive and specialized hardware.

DeepScale has a standard software licensing model that charges per sensor and per car to the manufacturer.  Their technology has already been licensed by a number of manufacturers and will be in customer cars in the next few years

The team is around 20 employees and is based in California. 

Why I like Them

What most people don't realize is that existing autonomous vehicles such as Waymo's are massively expensive.  The industry isn't cost-sensitive currently but it will have to become extremely cost conscience as it scales.  The current modus operandi for solving deep learning challenges is to throw more expensive hardware at it.  Automotive manufacturing is a low margin business and would never reach close to profitability with autonomous cars cost structure as they are being developed today.

 I like that DeepScale has thought ahead on this and is focused on creating computer vision systems that work on cars existing electronics (known as ECUs) rather than requiring additional specialized and expensive chips to be installed.  Under the current cost structures of automobile manufacturers, autonomous vehicles will not be able to be sold even close to current car prices, so the team's focus on the business side of the technology is particularly noteworthy.  By using systems already in the latest vehicles, they help automotive manufacturers save on cost by doing more with less.  Think of DeepScale as building efficient automotive AI.  

I also like the competitive nature of the product.  Their systems continue to gather data in the field and are able to be continuously updated with upgraded neural networks pushed to cars remotely that have already been sold.

Disclosure:  I have spoken to members of the team.

Edgybees

Overview

Edgybees is an augmented reality startup focused on enterprise augmented reality for fast moving camera systems.  This include cameras on drones, cars, military vehicles, and body cameras.  They've found great initial traction among public safety organizations including fire,  search and rescue, disaster response, police departments, etc. with their product showing great value during the California fires and the Hurricane Irma floods.  Other customers they are engaging with are in media, defense, gaming, and other fields where situational awareness is critical. 

Edgybees software platform is flexible, able to work with any full motion camera hardware with the ability for other developers to build upon it.  Features include mapping, markers, and other data layer overlays with new features being continously added.  The team monetizes by selling software licenses per vehicle pilot, people on the ground, and people in the command center. 

The team is based in Palo Alto, California and Israel and currently has around 20 employees.  

Why I like Them

I like them because to date I believe augmented reality has been overhyped without any real application that solve an actual problem.  Edgybees solves the real world problem of situational awareness for when speed and accuracy are critical and thus has a real business built on solving actual customer pain points.  

This is also an area where there is a ton of demand for a solution like this and few competitors.  With more and more cameras everywhere generating a torrent of data, technology like Edgybees is vital in bringing context and identifying what is critical in real time so decision makers can quickly make the correct decisions.  

I agree with the team that drone technology and augmented reality are still in very early days and I can easily see Edgybees becoming a very large enterprise augmented reality and sensor company.

Disclosure:  I have spoken to members of the team.