Chef Robotics Launches New Container Placement Quality Assurance Feature For Meal Assembly

(MENAFN- PR Newswire) The feature gives food facilities visibility into placement imagery collected on every meal that Chef assembles

SAN FRANCISCO, Sept. 20, 2024 /PRNewswire/ -- Today, Chef is launching a new food placement quality assurance feature, Placement QA.

Placement QA will allow food companies to see images of every meal assembled by Chef robots in their facilities, highlighting the placement of ingredients within each meal's container. Utilizing computer vision, Chef systems track containers on a production line and capture un-occluded images before and after food has been plated within them.

Utilizing computer vision, Chef robots track containers on a production line & capture images of every meal assembled.

The feature provides food companies with the ability to retain imagery data on every single ingredient deposit and meal assembled at their facilities. Chef already provides food companies with data on "pick weights" (the weight of each ingredient deposit); now, food companies can combine pick weight data with real-time imagery to paint a full picture of the quality of each meal assembled.

"With Placement QA we can help our customers get access to meal level data that they've never had before. Placement QA can help them reduce missed deposits and increase quality, ultimately increasing lifetime value of customers and reducing churn." says Rajat Bhageria, Founder & CEO of Chef Robotics of the new feature.

Rather than the traditional method of selecting meals on a line at random to inspect, now food companies can complete computational QA for every meal produced. This allows them to assess each meal across pick weight and image data metrics, setting scores for each ingredient deposit. Scores can be based on factors like whether the food was placed, how accurately it was placed within the proper compartment of a tray, and the quality of how food was spread or clumped.

In the event of an incident like metal or plastic contamination, food companies can browse their image library by timestamp and pinpoint the exact time an incident has occurred. This allows them to isolate the occurrence of an incident to a specific timeframe and inspect meals that may have been affected accordingly, saving them from the expense associated with having to throw out an entire production run's worth of meals and further reducing labor overhead as a direct result of reducing the number of employees needed to conduct spot checks.

About Chef Robotics
Chef is the first company to have commercialized a scalable AI-driven food robotics solution. With over 25 million servings made in production, Chef leverages ChefOS, an AI platform for food manipulation, to offer a recurring revenue solution that helps industry-leading food companies increase production volume and meet demand. Headquartered in San Francisco, CA, Chef aims to empower humans to do what humans do best by accelerating the advent of intelligent machines.

Media Contacts
Rajat Bhageria
[email protected]

Sarah Mowad
[email protected]

SOURCE Chef Robotics

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