Augmenting the Modern Workforce with Computer Vision

AI comes in many forms – from machine learning to neural networks to computer vision.

Computer vision is the ability to extract meaning and intent out of visual elements, such as faces, objects, scenes and activities. The objective of computer vision? To Help. To Assist. To Augment

Our company, Deepomatic, a computer vision company founded in Paris, recently launched in the North American market. Our proprietary technologies, Deepomatic Studio® and Deepomatic Run®, provide companies with the tools – both in the form of software and managed services – to build, operate and deploy their own enterprise-level artificial intelligence applications. 

In the European market, we work with global organizations, including Airbus, Belron, and the Compass Group, on a number of use cases, from automated checkout to smart CCTV. In the North American market, we are focused on enabling the augmented worker to achieve a more seamless workflow through computer vision technology in industries including insurance, telecommunications and quick serve restaurants (QSR). Highlighted below are just a few of the many uses cases for the computer vision-powered augmented worker in the US market. 

According to a recent report from Business Insider, in the insurance industry, the most valuable area in which insurers can innovate is the use of AI, which is estimated to drive cost savings of $390 billion by 2023. The report also states that the cost savings attributed to using AI will allow insurers to refocus capital and employees on more lucrative objectives. When it comes to the insurance industry, agents and adjusters can utilize computer vision technologies to optimize the claims process, through the automation of damage detection. For example, computer vision is able to give a visual understanding of the type of damage and condition of a car, a house, or heater to provide agents with more accurate criteria to base their claims on. 

With the arrival of 5G and the FCC’s recently announced rural broadband initiative, there will be an increased need for in the field training and guidance for technicians that are working in rural areas that may not have the infrastructure and resources those in urban areas typically have access to. With that in mind, telecom companies can utilize computer vision technology to provide real-time training and feedback in the field for their technicians. Additionally, computer vision technologies enable companies to automate the quality control of installations to cut down on technician re-interventions and improve the customer experience.

Finally, those within the QSR space can leverage computer vision to provide automated checkout solutions. These intelligent cash registers can reduce the time spent at the check-out to a few seconds. These smart checkouts are easy-to-use cash register terminals equipped with a camera and trained neural networks that are able to recognize more than 10,000 products and/or recipes. By removing the constraints of traditional checkout, the smart checkout can improve the customer experience within quick serve restaurants by reducing queues to fewer than 10 seconds per customer, in some cases. This allows employees within the QSR industry to focus on food prep and providing customers with a more personalized and attentive experience, especially as the industry increasingly turns towards a focus on delivery. 

AI is now ready to augment human workers and free them up to do more creative and essential tasks, rather than laborious and mundane tasks. Computer vision is one of those subsets, and as we expand within the North American market, we will look to expand our use cases and enable augmented workers within a multitude of additional industries.


About the Author

Jesse Mouallek – Head of Operations for North America, Deepomatic. Corporate finance background with 4+ years of enterprise sales operations experience in new technology. Building a career by taking the best of both. 

Featured image: ©sp3n

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