Artificial Intelligence is making its way into our food system in a big way. It’s on dairy farms monitoring milk quality, in restaurants powering food-running and burger-flipping robots, and even in the kitchen, walking you through a recipe in the guise of a voice assistant or chatbot.
Lately, we’ve noticed AI playing another role in what we eat: this time in flavor development. We’ve rounded up 5 startups merging AI and flavor to help restaurants and consumers create more sophisticated dishes, teach home cooks how to make dinner, and reduce friction for food R&D.
Foodpairing is a platform which uses machine learning and data analysis to create a sensory map detailing which foods taste good together. Since roughly 80% of taste actually comes from smell, they base their findings on the aromas of each ingredient. The Foodpairing Inspire Tool allows their customers—mostly professional chefs and bartenders looking to create innovative, unexpected dishes no one has tasted before—to discover pairings of the more than 1,500 ingredients in their database.
This app (which is currently available exclusively on their website) grew out of an ex-consultant’s desire to teach himself how to improvise in the kitchen. Using flavor mapping technology similar to Foodpairing’s—both are based around aromas and use machine learning—the platform allows users to select complimentary ingredients based on what they have in their kitchen. Once the selection is complete, the algorithm generates a custom recipe. The Copenhagen-based startup hopes to use their AI-driven platform to promote plant-based cooking and reduce food waste.
Self-described “food AI company” dishq uses customer data, machine learning, and food science research to predict consumer taste preferences. They translate their findings into APIs to help their clients, which range from food delivery platforms to corporate cafeterias, provide tailored food suggestions to their customers and outline emerging food trends. As co-founder Kishan Vasani told the Spoon, dishq offers “taste analytics as a service,” allowing companies to react quickly to food trends as they are happening.
FlavorWiki uses analytics to measure consumer taste and dietary preferences. They aim to unlock new applications for taste data across the food system. While they market themselves to a wide audience—everyone from major food companies to moms with picky kids—their taste-profiling technology is chiefly aimed at retailers. By creating self-described “taste archetypes,” FoodWiki hopes to help clients like CPG companies cut down on R&D costs for new products, reducing the pricey trial and error stage. If you’re curious about how exactly the FlavorWiki system works—and where it hopes to go—give our podcast with their CEO and Head of Product Daniel Proz a listen.
Gastrograph is another company using AI to help food & beverage producers streamline new product development. Their technology maps the flavor preferences of individual consumers and also predicts broader consumer reception to new taste profiles. Gastrograph hopes to help create only slam-dunk food products by using machine learning and predictive algorithms—no more costly duds. If you want to hear more about this AI-driven food tech company, check out our podcast with Gastrograph CEO Jason Cohen.
For food startups and CPG developers struggling to differentiate themselves from their competitors, services that use AI to predict and develop delicious, memorable foods would be a useful investment. If flavor/AI companies can deliver on their promises—to cut R&D costs, to help chefs and home cooks create tasty recipes, and to predict emerging food trends—they could be that extra something that spells success for emerging companies, or for big food giants whose current products are starting to feel stale. Flavor/AI technology could also play a huge role in predictive restaurant ordering or grocery delivery, both of which Amazon has in the pipeline.
The bottom line for food industry folks, if you don’t have a taste for AI, you’d better develop one—and soon.