Menus are becoming smarter, and as restaurants manage hundreds of SKUs and ever-evolving guest preferences, AI recommendation systems are transforming the way customers browse, order, and return for more.
For operators, this could mean higher ticket values, less friction, and better loyalty metrics.
In this article, we’ll unpack how iFood’s AI-powered engine nearly doubled add-to-cart rates, highlight similar moves from chains like Applebee’s and Taco Bell, and peek under the hood at the tech infrastructure that makes up these modern recommendation systems.
iFood’s AI System in Action
iFood, Latin America’s largest food delivery platform, recently deployed an advanced AI recommendation engine known as RED (Recommendation Engine for Dishes). Built on the LightGBM gradient boosting framework, RED leverages a wide range of features, including dish metadata and pricing, user behavior, and real-time contextual signals like time of day and order history.
In rigorous A/B testing, RED significantly outperformed traditional recommendation approaches. Compared to popularity-based models, RED boosted card conversion rates by 97%, meaning nearly twice as many users added suggested items to their carts. It also delivered a 1.4% lift in overall conversion rate. And while that number may not sound notable, remember that even fractional improvements at iFood’s scale translate to millions in revenue.
Technologically, the system operates on a mature MLOps pipeline integrating AWS SageMaker for model training and deployment, and Redis Cloud for lightning-fast inference and real-time feature serving. This tech stack helps the engine deliver updated recommendations within milliseconds, adapting on the fly to each user’s context and preferences.
Everyone Is Jumping on the AI Menu Bandwagon
Turns out, iFood isn’t the only major company using AI to nudge hungry humans toward better ordering decisions.
At Dine Brands, which owns Applebee’s and IHOP, recommendation tech is already being tested in real-world environments to tap into past purchase behavior and similarities across customer profiles, and suggest dishes guests are more likely to want, even before they think to search.
Paired with AI-powered cameras that flag dirty tables and digital tools to help managers optimize operations, you’ve got the early blueprints of a restaurant that kind of runs itself. We’re a ways away from robots in aprons, but we’re making strides.
Meanwhile, Papa John’s is rewriting the pizza playbook with Google Cloud, serving up personalized push notifications, smarter chatbots, and hyper-targeted loyalty rewards, all powered by AI. Their future roadmap includes virtual ordering assistants and predictive inventory tools, with the ultimate goal of removing friction from every part of the customer journey.
Zoom out even further, and you’ll see Yum! Brands (the conglomerate behind Taco Bell, KFC, and Pizza Hut) has rolled out a platform called Byte, which is essentially a kitchen command center that integrates voice ordering, real-time analytics, and computer vision tools to make drive-thrus smarter and faster.
What all of this tells us is that recommendation engines will likely become the new secret sauce for guest satisfaction and bottom-line growth, whether in the drive-thru or the dining room.
How Recommendation Systems Work
So, how do these recommendation systems actually know what you’re craving before you do? It all comes down to a clever mix of data, algorithms, and infrastructure.
At their foundation, most of these engines combine collaborative filtering (which recommends items based on what similar users liked) with content-based filtering (which matches ingredients, categories, or tags to your personal history).
Think of it like Netflix for food: “People who liked spicy chicken tacos at 6 p.m. also enjoyed…”
iFood’s RED system takes things a step further with a gradient-boosted decision tree model known as LightGBM, which crunches hundreds of variables, from dish price and cuisine type to whether it’s lunchtime on a Monday or dinnertime on a payday, to predict what you’re most likely to tap next.
One big challenge these systems face is the so-called cold start problem, which is, “How do you recommend a dish with zero reviews?”
RED tackles that using content embeddings and smart sampling techniques that avoid just pushing the most popular items. As such, it’s able to stay relevant even when the menu changes daily.
Of course, none of this works without million-dollar technology under the hood. Lightning-fast infrastructure like Redis Cloud powers real-time suggestions, even when millions of users are browsing at once. It’s the difference between “instantly knew I wanted dumplings” and “laggy menu that guessed wrong.”
The Benefits to Operators
Now, while there may be a ‘cool’ factor to all of this, the main goal is cold, hard results.
iFood’s jaw-dropping 97% spike in card conversions after launching its RED recommendation engine was not a rounding error. It was real revenue, all from smarter upsells that actually made sense to the customer.
Of course, it’s not just iFood seeing green. Restaurants that personalize upsells, such as a drink that pairs with your burger or a promo on something you reorder weekly, are seeing similar results. According to Panera Bread, personalization is now a core driver of customer satisfaction.
Beyond the guest experience, these tools offer numerous operator benefits. They can nudge orders toward items with surplus inventory, reduce pressure on overburdened kitchen stations, and help test promotions in a way that actually changes customer behavior.
Getting Started With AI
If you’re thinking about tapping into recommendation AI, start with your data. Your guest profiles, POS transactions, and inventory history need to be clean and consistent. Garbage in, garbage out. Once that foundation’s set, run a pilot. The best early wins are smart bundles. Suggesting sides, desserts, or popular add-ons can lift order values without feeling pushy.
Next, make sure your recommendations actually connect with your POS, loyalty programs, and kitchen display systems. It’s no good suggesting fries if they don’t make it to the line. And once you’re live, track everything, from card adds and order size to item mixes and customer feedback. The goal is to build a smart system that keeps getting smarter over time.
And if you need a hand, our team at EMERGING can help. We work with restaurant brands to help them partner with technology firms, so they can test, launch, and scale technologies like recommendation AI.
Get in touch with us, so we can help you build a smarter strategy together.
