Develops remote assistance, collaboration, quality tracking, and other enterprise solutions based on the company's head-up display (HUD), software platform, and suite of proprietary and external apps
Differentiated for target heavy-industry use-case through improved durability, form factor, and...
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Develops an AR smartphone app that allows users to create 3D digital objects with embedded content and to share them with other users by overlaying them in physical space
Digital objects include 3D cubes, a life-size rendering of an individual that can be used to leave voice messages, and a 3D life-size model of a physical object that can be used to market products
Aims to monetize the app for retailers by charging fees to place digital content in specific physical locations; for consumers, it will provide a freemium model with additional digital objects available for purchase
Consumers can use Hoverlay's technology as a testing ground to see what will work, but should seek to work with larger players in the long term; indoor position systems (IPS) solution providers may benefit from partnering with this company
Develops a software toolkit that enables developers to quickly build time-series machine learning algorithms embeddable on microcontrollers to create smart sensors
Claims differentiation in that its models are sensor agnostic and can run on lightweight microcontrollers and that its solution is comprehensive for developing models
Primarily targets Industrial Internet of Things (IIoT), wearables, smart home and smart cities, and agriculture
The company uses a licensing business model; licensing fees range from under a dollar to several dollars per final device sold
Clients should view the company positively given its comprehensive solution for building machine learning models on microcontrollers; however, they should note that its sales and marketing efforts lag behind its technology
Cloud-based platform for R&D engineers to build machine learning models for sensor signal processing on embedded devices; focuses on classifying, detecting anomalies, and making predictions with sensor data
Once the models have been trained, a customer can begin using the model through an application programming interface (API) hosted on Reality AI's cloud, or the customer can compile and export the code to be embedded onto a microcontroller
Targets customers in the automotive, industrial equipment, and consumer devices markets
Charges a yearly subscription fee of USD 60,000 to USD 180,000 for access to its platform as well as a licensing fee of several tenths of a cent to several dollars for each final unit sold
Clients interested in developing machine learning models for sensor data should consider engaging with the company because of its differentiated technology in allowing nonexperts to develop machine learning models for edge devices