AI is helping quick-service restaurants (QSRs) address labor shortages, drive operational efficiency, transform the customer experience, and become more agile and efficient. With computer vision and speech recognition technologies, QSRs can automate food ordering through AI-enabled branded avatars, predict when orders will be ready, optimize staffing, and deliver food faster—creating a comprehensive end-to-end customer experience.
To address challenges and meet consumer expectations, QSRs are turning to AI. Learn about the most important use cases today.
Learn how avatars can increase order size by 15 to 18 percent and extend operation hours from 12 hours to 16 hours or more.
Don't miss these three upcoming Restaurant sessions at GTC.
Real-time interactive 3D avatars can deliver natural and engaging experiences that make people feel more comfortable. But building lifelike digital humans requires a broad range of expertise–including computer graphics, AI, and DevOps.
In this session, we will showcase how developers can use new Retail Pre-trained Models and Metropolis SDKs and microservices to develop Retail applications for Loss Prevention. Developers will learn how to leverage the pre-trained models as is as well as how to think about how to customize and fine-tune them for a variety of use cases.
Online food-delivery platforms are expanding their choices, allowing customers to order from a wide variety of restaurants. Meituan owns one of the world's largest multi-person, multi-points, real-time intelligent delivery platforms. Route planning is its most critical part regarding distribution efficiency, cost-saving, and user experience. As an industrial-level technology, route planning has to be fast enough.
Learn about AI hardware and software for QSRs.
The NVIDIA Tokkio reference workflow—built with NVIDIA ACE—leverages Riva NIM microservices for speech, Nemotron NIM microservices for intelligence, and Audio2Face NIM microservices for animation. With ACE Agent, easily connect to vision models, recommenders, and NVIDIA Omniverse™ RTX microservice to elevate the customer service experience.
Due to labor shortages and high volumes in drive-throughs, restaurants are bringing automation into the kitchen with NVIDIA Metropolis vision AI and NVIDIA independent software vendor (ISV) partners. These advanced solutions are providing real-time guidance into food production quality and waste, ensuring the accuracy of orders, and delivering insights to help managers improve store efficiency and ensure a consistently positive customer experience.
RAPIDS allows data scientists to train more models, iterate quickly, and scale services and shared knowledge. Restaurants can run forecasts more frequently and improve forecast accuracy by as much as 20 percent to ensure they have the right products and food quality, waste, and safety are being monitored.
Every single fast-food restaurant and retail store should have automated avatar agents that represent each brand to provide excellent customer service.
– NVIDIA CEO Jensen Huang on an episode of Jim Cramer’s Mad Money
NVIDIA announced major advancements to OpenUSD that will expand adoption of the universal 3D data interchange framework and accelerate the ability to accurately build virtual worlds for the next evolution of AI.
WPP, The Coca-Cola Company
Marketing leader WPP is enabling the beverage giant to accelerate iteration of creative campaigns at global scale with NVIDIA NIM™ microservices, USD Search, and USD Code.
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Learn more about how NVIDIA AI is bringing leading-edge technology to our everyday retail experiences.
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Learn how to build and execute end-to-end, GPU-accelerated data science workflows that let you quickly explore, iterate, and move your work into production. In this self-paced lab, you’ll learn how to use RAPIDS to perform data analysis at scale with a wide variety of GPU-accelerated algorithms.
Take this self-paced lab to learn how to interact with the NVIDIA® Riva speech server to process various conversational AI requests. You’ll learn how to send audio to an automatic speech recognition (ASR) model and receive back text, use natural language processing (NLP) models to transform and classify text, and send text to a text-to-speech (TTS) model and receive back audio.
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