Deepomatic Releases QSR Computer Vision Use Case as Speed & Accuracy Numbers Decline in 2019
Deepomatic, a leading computer vision and image recognition company, has announced its solution for the quick-serve restaurant (QSR) industry. The newest application of the company’s SaaS computer vision platform addresses the two biggest issues for franchises within the space – speed and accuracy – and comes at a time when both are declining. The prevalence of these issues has ultimately caused notable franchises to begin offering steep discounts and free items to make up for their inaccuracies.
According to the 2019 Drive-Thru Performance Study, average order accuracy fell just over five percent from last year, while customers spent 20 seconds longer waiting for food than they did in 2018. The answer lies in AI and machine learning. In their Understanding the QSR Customer Through a Digital-first Strategy white paper, Samsung stated that “QSRs can use AI and machine learning to improve the guest experience and increase revenue while simultaneously reducing operational complexity.”
Currently, Deepomatic’s software enables businesses to automate their visual processes through image recognition. Their products allow operational business managers to build custom AI applications and operate them at scale, within 3 months and without coding skills.
Deepomatic’s GM of North America, Jesse Mouallek concurred, stating that, “the QSR space is ripe for the implementation of AI, particularly computer vision. We’ve consistently heard prospective clients within the QSR space echoing what Samsung said in their report, but they have been unable to find a partner who can provide a ready-made solution for them to implement without considerable groundwork already completed on their end. We can now offer that solution for them.”
The implementation of computer vision can enhance the entire customer journey, while reducing customer care costs for franchises. From in-store to the drive-thru, computer vision starts at the line. With the ability to alert staff of line length at either location, restaurants can be assured that their staff are servicing the correct segment of the business and executing production sprint at the correct moments – reducing customer wait times.
When it comes to accuracy, computer vision can be used to verify the conformity of each order on assembly areas – ensuring the correct number of items and verifying any special requests – like food allergies. These smart cameras alert human workers, so they don’t miss any items – from sauces to dips and ingredients. Reducing the number of mistakes not only increases customer satisfaction, resulting in return customers and higher revenue potential, but also drastically reduces the cost of customer care when orders are incorrect.
“By optimizing order assembly and order preparation, computer vision empowers quick serve restaurants to maximize both speed and accuracy of delivery, aligned with our KPI of shorter wait times,” said the Director of Operations and Development Analytics at a leading food service company in regards to their interest in the application.
Originally founded in Paris, France in 2014, Deepomatic focuses on the real-world applications of AI in image recognition and computer vision – specifically on the industrial level, making tasks more efficient. Deepomatic raised a $5.1M Series A round in February of this year to fund the North American expansion – led by Mouallek.
The applications developed by Deepomatic clients are among the most advanced use cases deployed: The Compass Group (world leader in contract restaurant) created an AmazonGo-like smart checkout system reducing lines to 10sec per customer, improving guest experience. Bouygues Telecom automates optical fiber installations quality control for all their subcontractors.