Agot.AI Brings Computer Vision to Fast-Food
by Adam Pease
Recently, Agot.AI closed a round of funding for $10 million, putting it in a position to bring its innovative computer vision product to market for the fast-food industry. Agot bills itself as a ‘Kitchen Awareness’ solution that monitors workers and kitchen activity to optimize the preparation and delivery of food. In this blog, we review Agot’s recent funding success and consider what the product’s implications are for the integration of computer vision into the contemporary workplace.
Who Is Agot.AI?
Agot.AI offers a machine learning platform designed to improve revenue to businesses that depend on kitchen line work. Its tools are optimized for fast-food environments where large amounts of line workers or busy drive-thrus define the pace and process of business. Agot.AI provides proprietary cameras that can be installed in the ceiling of a kitchen and used to monitor the pace. Its computer vision solution can determine if orders are being prepared correctly, properly bagged, etc. Agot.AI can then issue interventions, notifying workers if a mistake has been made.
This solution is designed to produce a level of automated management for kitchens. Computer vision is a powerful tool for workplace surveillance. We are seeing the adoption of computer vision by large businesses in sectors where scalable solutions have always been at the forefront of mind for business leaders. In these sectors, such as fast food and grocery retail, we can visualize a test case for the adoption of computer vision more generally in the workplace.
Computer Vision and Employee Monitoring
The fast-food industry has always been notorious for the pressure it can put on workers. Fast-food workers already have their performance measured in a number of concrete ways, and the industry has been careful to implement strict management protocols designed to secure efficiency.
What remains to be seen is how workers will respond. On the one hand, the preexisting metrics and tight management surveillance that typifies fast-food work may mean that workers exercise little resistance. Many may see such shifts towards camera-based surveillance as inevitable or expected. On the other hand, the shift to real-time, active monitoring may upset workers who value their privacy or autonomy more, which could impact the hiring prospects of enterprises that employ such solutions.
It is unclear how employees of major fast-food companies would react to the around-the-clock surveillance provided by computer vision vendors like Agot.AI. At the same time, the capacity to monitor workers in this way is doubtless an attractive revenue opportunity, and a chance to cut costs or free up managerial resources.
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