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The Spoon

Daily news and analysis about the food tech revolution

artificial intelligence

Gardyn Aims to Make At-Home Vertical Farming Small, Simple, and Stylish

Thanks to disruptions in the food supply chain, panic-buying sprees, and the general uncertainty of the times, growing food at home seems like a pretty good idea of late. Trouble is, many consumers don’t have the know-how to cultivate their own leafy greens and other produce in the backyard. Even those who do often lack adequate space.

A company called Gardyn is addressing both of those issues with an at-home vertical farming system that requires minimal input from the user and can easily fit inside a small apartment if need be. The idea, as Gardyn founder and CEO FX Rouxel explained to me over the phone last week, is to make growing food in one’s own home as simple and straightforward as possible. To do that, the company has built a farm that relies on AI to do much of the heavy lifting in terms of monitoring and maintaining an edible crop of food. Or as Rouxel said, “The system is managing everything for you.”

Gardyn’s system is made up of two parts: a compact vertical tower, which can grow as many as 30 plants, and an accompanying app powered by an AI assistant named “Kelby.” Users only have to order seeds and “plug” the seed pods into the vertical towers. The system automatically circulates water and nutrients to the plants, while Kelby monitors plant growth and sends reminders when it’s time to add water to the garden or harvest the plants. 

Right now, available crops from Gardyn’s site include mostly leafy greens and herbs, some flowers, cherry tomatoes, and jalapeños. Customers can also use their own seeds if preferred.

The system uses what Rouxel calls “a hybrid of different hydroponic technologies,” including the deep water method and aeroponics. (The company brands its approach as “hybriponics.”) By themselves, these different methods have certain limitations in the at-home setting. Deep water, where plant roots are fully submerged in nutrient-enriched water, requires a lot of space. Aeroponics is a great setup for outdoors, but once indoors it requires lighting, which gets expensive very quickly. Gardyn pulled elements from both to create a system that takes up only two square feet of space and doesn’t require any extra hardware. “Within just two square feet, you can produce a lot of food,” says Rouxel, adding that Gardyn’s units have produced “over 25,000 pounds of produce” during the last few months.

That quest to grow a lot of leafy greens in a small amount of space is an area with plenty of competition these days. Farmshelf recently unveiled its first-ever farm for the home, and companies like Rise Gardens and Agrilution (the latter recently bought by Miele) also offer promising solutions for the consumer space.

And while historically, investment in vertical farming has mainly gone towards the industrial-scale indoor farms (think AeroFarms), at-home farms are fast becoming a lucrative area. Investors, Rouxel explained to me, see traditional agriculture as a risky business that’s less insurable because its success is in part dependent on the weather outside. With climate change triggering more extreme weather, investors will look more and more to alternative solutions in controlled-environment agriculture.

“I am absolutely convinced we are going to see in the coming two years a total disruption in the way we grow things,” he says. Chiefly, that will be growing the food in much closer proximity to consumers, whether through at-home systems like Gardyn’s, in-store farms at grocery retailers, rooftop gardens, and high-tech greenhouses. “In future we’re going to have a spectrum of solutions,” Rouxel noted.

Getting these vertical farms closer to consumers and in their own homes will require bringing the price of the machines down. At the moment, Gardyn’s system is roughly on par pricewise with other systems out there that can realistically feed a family of four: $799 for the base model all the way up to $1485 for the “Plus” model.

Rouxel is aware that the cost is still too high for many consumers. “We don’t want this to be only for well-off people,” he told me. “It’s important that we find ways that anyone can afford this.”

Many companies, including Gardyn, offer financing options on their farms now. And more investment dollars going into the space in the future could mean companies have the time and space to innovate on ways to make their system cheaper for the average consumer.

While pricing remains a question, one thing that’s certain is that at-home vertical farming is on the path to becoming a regular part of the kitchen, rather than just a trend. “What we want is to develop solutions that will quickly change the way people access food,” said Rouxel. “We won’t solve everything, that’s for sure, but we want to be part of the solution for how we shape food.”

Tastewise Launches Its AI-Powered Food Prediction in the UK

Tastewise, which uses AI to predict consumer food trends, announced today it has launched its platform in the UK and added more localized data to help restaurants and food producers in that region better anticipate consumer eating habits. According to a press release sent to The Spoon, Tastewise has integrated data from 183,000 restaurants and delivery menus, over 2.8 billion social interactions, and 1.2 million online recipes from the UK into its platform.

The Tastewise platform, which launched in February of 2019, helps CPG companies better predict food trends through artificial intelligence (AI). It analyzes consumer touchpoints (think Instagram photos and online recipes) to find not just what foods consumers are eating right now but also their deeper motivations for choosing those foods. When we spoke about a year ago, Tastewise CEO Alon Chen used the example of sauerkraut to illustrate the point: 

“Right now, according to [Chen], it’s a popular food, but the trend is less about raw cabbage and more about the process behind it, which is fermentation . . . Food companies analyzing data via the Tastewise platform can see such data and consider how they might implement fermentation into their offerings.”

Tastewise counts Nestlé, PepsiCo, General Mills, Dole, and other major CPGs among its customers. With today’s launch, UK-based food businesses will also be able to utilize the platform to get more real-time insights into consumer food behavior, which is a must in these pandemic-stricken days, according to Tastewise: “The pandemic has made it clear that it’s imperative to have your finger on today’s pulse each time a decision is made,” Chen said in today’s press release.

Speaking of which: along with the news of its UK launch, Tastewise also released a new report on UK consumer food trends during the pandemic so far. Among them:

  • Sustainable foods are rising 52 percent in popularity year over year, though health and fitness are motivating this trend, not environmental concerns.
  • UK demand for meal kits is up 200 percent.
  • CBD is fading in popularity.

Tastewise is not a sensory platform a la Spoonshot or Climax Foods. However, it is similar in that it leverages AI to help companies bring in-demand products faster by sinking less money into traditional R&D. And given the upheaval the pandemic has caused across the food system, both CPGs and foodservice companies will be leveraging more AI in the future to keep costs down by offering their customers the most relevant items possible. 

ConverseNow Raises $3.25M for Its AI-Driven Restaurant Ordering Platform

Restaurant tech startup ConverseNow announced this week it had raised a $3.25 million seed funding round for its platform that uses AI to automate the process of ordering food. The round was led by Bala Investments with participation from LiveOak Venture Partners, Tensility Venture Partners, Knoll Ventures, 2048 Ventures, Bridge Investments, and Delphi Display Systems’ CEO Ken Neeld. It brings ConverseNow’s total funding to date to $3.3 million. 

ConverseNow’s CEO Vinay Shukla says part of the Austin, TX-based company’s new funds will go towards improving the AI that powers its platform. 

That AI allows restaurants to automate and personalize the ordering process for customers. Restaurants can integrate it across multiple sales channels (drive-thru, mobile app, etc.) to increase things like order accuracy and make better personalized recommendations based on a customer’s order history and other data. The platform integrates with restaurant POS systems as well as back-of-house kitchen displays.

AI is a hot topic when it comes to speeding up service and improving order accuracy in the restaurant. McDonald’s put the conversation squarely in the spotlight last year when it acquired Dynamic Yield and installed the company’s AI tech in its drive-thrus. Starbucks has in the past claimed AI is “a very important part” of its overall strategy. And a survey from the end of 2019 found that 71 percent of customers are “amenable” to having more AI in their restaurant experience.

If the same survey were given now, that figure would probably be higher. The COVID-19 pandemic has forced most restaurants to pivot to off-premises orders, and even as dining rooms slowly reopen, states’ guidelines recommend keeping to-go meals a priority. That in turn will mean more people going to the drive-thru and ordering via off-premises channels such as websites, mobile apps, and even the good old-fashioned telephone.

The other plus of AI right now is its ability to increase contactless ordering and payments. In the restaurant tech stack, it’s the tools that can offer more seamless ways to provide these things that will provide the most value.  

ConverseNow said in the press release that its tech is already being used by “leading QSRs.” In addition to improving its AI platform, the company will also use the new funds to improve customer acquisition. 

Expect More Restaurants to Use AI Cameras Like DragonTail’s to Show a Kitchen’s Cleanliness

Before the COVID-19 pandemic, people mainly worried about where their food came from. Was it organic, locally raised, grain-fed, etc. (It was a simpler time.) But as the virus has spread, the bigger concern for consumers is who has touched their food, and were they wearing gloves and a face mask when they did so.

This is the socially distant, contactless delivery world that we now live in, which actually makes it the perfect time for Dragontail Systems to launch its new AI-powered camera that detects the sanitary conditions of food prep areas as the food is being packaged up for delivery.

You may remember Dragontail when its camera + computer vision system debuted at Domino’s in Australia last year. Dragontail’s camera is mounted above the workstation in Domino’s back of house where pizzas come out of the oven, are sliced up and boxed.

Back then, the Dragontail system was being used to assess quality control: that the pizza was the right shape with the right toppings, and that it was cooked properly. Dragontail’s camera took pictures of the pizza and Domino’s sent those pics to the customer as a way of showing hey, your pizza order was made properly and it’s heading out to you.

Now, in addition to the existing quality control features, Dragontail announced today that its camera system can check for sanitation conditions. The camera detects things like whether gloves and facemasks being worn, or how often a workspace is sanitized. The exact parameters of what to look for are up to the restaurant, as are how violations are communicated to the worker and/or manager.

So now, in addition to a picture of the food cooked properly, restaurants can provide a customer with a checklist of steps taken to show that the food was also handled properly. The end customer, then, can feel a little more comfortable knowing the restaurant followed proper cleanliness procedures.

We actually started seeing similar technology pop up last year in, of all places, China. As we wrote then, AI systems were installed in restaurants in the Shaoxing Province of China to monitor for unsanitary conditions like improper uniforms and mixed use of cutting boards. More recently in India, home cook marketplace FoodCloud launched a Kitchen Cam, which offers customers video footage of the kitchen and cooks as they make and package food.

Dragontail’s camera is currently being used in more than 2,500 stores across Australia, Canada, Singapore, the UK and Belgium. With today’s announcement, the company is ready to expand into the U.S. and work with restaurants of all sizes. The cameras cost between $500 and $1,000 and there is a $50 monthly fee for the computer vision systems.

As we’ve written before, the COVID-19 pandemic is accelerating changes throughout the meal journey. With cameras like Dragontail’s likely becoming more commonplace, the meal journey will now include pictures.

KitchIntel Is Using Software and AI to Help QSRs Meet Demand for Protein

Nowadays, quick-service and fast-casual chains are under especially heavy amounts of pressure when it comes to meeting customer demand for quantity without sacrificing quality. Thanks to tech, managing — and predicting — that demand is getting a little easier.

Case in point: Houston-based enterprise tech consulting firm Smartbridge launched KitchIntel, a software solution for restaurants to more precisely monitor their proteins, in 2019. Smartbridge’s Marketing Manager Brooke Browne and the company’s Director of Digital Innovations Deepthi Raju got on the phone this week to talk me through how the system works.

Backing up for a second, Browne and Raju told me the company has chosen to focus on protein for now because chicken, beef, and other meats form the base of so many dishes on QSR menus. As well, most proteins spoil easily and have to be cooked in certain ways to ensure food safety.

The KitchIntel software takes a lot of the guesswork out of the cooking and ordering processes by telling cooks how much food to cook when and also sends them alerts at key steps in the cooking process. A dashboard interface displays which proteins are currently on the grill, how long they have been cooking, what to cook next, and even when to flip the meat over.

All of that information comes from data inputted in the system. In the case of how to cook the proteins, that information would come from corporate, based on company recipes. “The beauty of this application is that it has the benefit of standardization as to how [restaurant chains] cook their main proteins,” Raju said on the call. “It reduces the amount of time the manager has to spend on training the new employee.”

KitchIntel is at this point most useful to quick-service and fast-casual restaurant chains that have to cook said proteins in huge batches over an extended period of time in order to meet customer demand. Think restaurants that specialize in slow-cooked chicken dishes or a chain like Chipotle that has to keep large quantities of protein on hand around the clock. At the moment, the company is more focused on these types of chains than one like, say, McDonald’s, whose operations more involve making single patties on the grill.

Perhaps even more significantly, a system like KitchIntel’s can help kitchens predict how much food they need to cook in the first place. The system integrates with other pieces of the restaurant’s technology stack, which means it can pull historical sales data from the POS to predict demand on any given day for any given shift. By way of example, Browne referenced one QSR client during our talk (name withheld) whose menu heavily features grilled chicken. After rolling the KitchIntel system out to over 130 of its locations, the chain has reported food cooked closer to its time of sale, ensuring fresher food for the customer and less food waste thanks to better communication with inventory systems and more accurate predictions of how much to cook.

Browne says the company also offers an AI component that can provide even more granular data than historical sales. As we’ve seen with companies like McDonald’s, who implemented Dynamic Yield’s AI tech into its drive thrus last year, using AI and machine learning to factor in weather and traffic data for each individual store can help restaurant chains make even more accurate predictions in terms of how many customers to expect. Five years ago, rainy weather might have meant a store didn’t prep as much chicken because it anticipated that most people who stay in. Nowadays, though, delivery orders often spike during rainy weather. AI that ingests weather data and feeds it into the KitchIntel system of a restaurant could help restaurants know exactly how much chicken (or beef or pork) to prep in order to better meet forthcoming demand. 

“A lot of decisions are made just by looking into the dining room,” Raju said. “That throws them off completely when there is a weather situation. While the dining room may look empty, it’s the drive-thru or the Grubhub people that are getting busy. So really incorporating AI into the sales prediction aspect of it is one thing that makes the solution unique.”

As demand for off-premises orders — delivery, takeout, drive-thru, etc. — drives the bulk of restaurant sales over the next decade, QSRs and fast-casual restaurants in particular will find themselves increasingly under pressure to deliver high volumes of protein-based meals to customers without sacrificing speed or quality. As in most parts of the restaurant industry these days, especially when it comes to behind-the-scenes operations, more data for smarter, more accurate predictions seems to be the key.

Survey: 71% of Consumers Are ‘Amenable’ to More AI in Their Restaurant Experience

Well over half of consumers “are amenable” to more artificial intelligence (AI) and advanced tech in their restaurant experience, according to a new survey from ad-tech firm AdTheorant.

The survey of over 2,000 U.S. adults, conducted this past September by The Harris Poll, looks at consumer sentiment and interaction with quick-service restaurants (QSRs) and fast-casual restaurants (FSRs) across a number of areas, AI among them.

Of those survey respondents, 71 percent said they would be “open to QSRs/FSRs incorporating AI into their business.” In particular, consumers would be most interested in AI if it could help drive down the cost of menu items (43 percent) and speed up the ordering process (42 percent).

As to the actual AI technologies that could do that, consumers are most interested in screens, according to the survey. Sixty-six percent said they were interested in using a touchscreen device (phone, kiosk, etc.) to order and nearly half of respondents, 42 percent, said they would like a voice-ordering system. 

Restaurants are already trying to meet this demand. Self-service kiosks are becoming a regular fixture at QSRs and FSRs as chains revamp their store formats to be more delivery- and takeout-friendly. In the last few months alone, we’ve seen Shake Shack, Chopt, Sweetgreen, and Krispy Kreme, among many others, unveil new store formats that feature kiosk ordering. Meanwhile, KFC is reinventing the concept of the drive-thru to be more touchscreen-centric, and McDonald’s leads the pack in terms of AI in the restaurant with its 2019 acquisitions of AI company Dynamic Yield and and voice-tech startup Apprente.

More surprising was the lower percentage of survey respondents who said AI offering more personalized food recommendations was important. On of the goals for McDonald’s when it acquired Dynamic Yield this past March was to make menus more “Netflix-y.” In other words, menus could dynamically generate recommendations based on a number of factors (past orders, trending items) and in doing so offer more relevant recommendations and upsell items.

AdTheorant’s report, however, notes that just 22 percent of consumers said this would be an important driver of their adopting more AI tech during their restaurant experience.

Part of that may be a matter of exposure. McDonald’s aside, many chains are still just getting started when it comes to the AI-driven menu. Dunkin’ is said to be dabbling with it. Starbucks says AI is a key piece of its overall digital strategy moving forward and that it’s Deep Brew initiative, which will (among other things) power better menu recommendations will be a big part of the chain’s focus in 2020.

Sony Sets up AI Unit to Work on Food

Sony announced this week that is has launched Sony AI, a new organization that will research and develop artificial intelligence specifically for games, imaging and sensors, and “gastronomy.” The new initiative will have offices in Japan, Europe and the U.S.

There aren’t many details around what exactly Sony will be working on, but Sony spokesman Shinichi Tobe told AlJazeera yesterday that “AI and robotics will not replace chefs. We are aiming to offer new tools to expand their creativity with AI and robotics.”

This isn’t Sony’s first foray into food. In April of last year, Sony teamed up with Carnegie-Mellon University to work on food robots. As we reported at the time:

Sony said they were starting off with food-related robots because the complexities involved with food could later be applied to a wider range of industries. Specifically, it cited the ability to work with fragile and odd-shaped materials, as well as the ability to operate a robot in small spaces.

AI and robots are like peanut butter and chocolate with AI being the “brain” for the robot “hands.” Things like computer vision, deep learning and synthetic data help form the AI so the robot can determine objects to grab and manipulate, etc..

Sony’s motivations may also be more societal in nature as the company’s home country of Japan is facing an aging population. Robots and other forms of automation could help with a potentially diminished labor force.

Food is a popular subject for robotics and AI researchers. Nvidia’s Lab in Seattle built a kitchen to train its robots to do everyday tasks. IBM partnered with spice company McCormick to use AI to develop new food products. And Korea’s Woowa Bros. hooked up with UCLA to work on food robots as well.

Something tells me we’ll be seeing more of these types of deal throughout next year.

Artificial Intelligence Is Now a ‘Very Important’ Part of Starbucks’ Digital Strategy

Artificial intelligence (AI) is one phrase we’re going to hear a lot more of in the restaurant industry from now on. McDonald’s and KFC are already experimenting with it, and this week, Starbucks said the technology is a key piece of the company’s overall digital strategy moving forward.

On an investor call, Starbucks CEO Kevin Johnson highlighted the company’s Deep Brew initiative, which will be a major area of focus in 2020. And as Johnson explained in a LinkedIn post recently, Deep Brew involves machine learning technologies that will improve back-of-house elements like inventory management and employee scheduling. Johnson said the technology will also power better recommendations and upsell offers to customers via the Starbucks mobile app. “Deep Brew solutions will support our partners in many ways such as sequencing orders, anticipating equipment maintenance, streamlining supply chain logistics, and more,” he wrote in the post.

On this week’s investor call, Johnson called Deep Brew a “key differentiator” for the future and said it will free up time for employees to focus on higher-quality interactions with customers.

That emphasis on customer service has been a major selling point from restaurants as they automate and digitize more of their businesses. From the aforementioned McDonald’s to fast-casual chain Sweetgreen, which just opened a digital-centric store in Manhattan, companies are claiming technologies like AI and robotics won’t replace workers’ jobs, but instead redeploy where workers spend their time and energy.

Certainly, Starbucks will have the opportunity to test this idea out as it builds out its Starbucks Now stores in China, which are locations that focus on digital pickup and delivery orders and where much of the order, pay, and pickup process is automated. The company opened its first Now location in July in Beijing. On the investor call, Johnson said the company will open more of these stores in China’s “top-tier cities” in 2020. Starbucks also has a location planned for NYC.

Chipotle ‘Quietly’ Introducing AI Into More Stores

Chipotle has been implementing more artificial intelligence (AI) into its stores this year in the form of AI-powered voice assistants for phone orders, according to Nation’s Restaurant News. The chain has already implemented the technology into 1,800 locations so far this year, and plans to have the system in remaining U.S. stores by the end of 2019.

The as-yet-unnamed female voice greets customers and takes orders. According to NRN, she “actively listens and makes suggestions when she thinks a customer might have omitted a topping on a bowl or burrito.” Over time, her algorithm learns and can remember more and more complex orders.

Chipotle has actually been quietly testing this technology out since 2018, when it introduced its “voice” to just 10 stores. But so far as AI is concerned, the time to be hush-hush about these initiatives might be coming to an end. Domino’s has been using a chatbot since 2018, an AI-powered system called DOM that also takes phone orders. And ever since McDonald’s acquired Dynamic Yield and started aggressively rolling the company’s tech into its drive-thrus systems (and soon its self-order kiosks), AI has been not a buzzword but the buzzword flying around QSRs and fast casual chains.

Having AI-powered tech in the restaurant has a few advantages. In the case of phone assistants like Chipotle or Domino’s, it can speed up the order process and minimize the amount of time employees have to spend on the phone (assuming the voice assistant can learn and retain complex orders and not need human intervention to process them). AI can also improve how well restaurants can offer more personalized recommendations to customers — that is, ones tailored specifically to that customer and based on everything from a person’s past orders to the weather outside.

What it won’t do, at least in Chipotle’s case, is take away jobs. Nicole West, vice president of digital strategy and product at Chipotle, told NRN that the phone-order system is meant to make the phone order process more convenient and save restaurant managers and employees time.

McDonald’s Installs Dynamic Yield’s Personalization Tech in 700 Locations

McDonald’s has wasted no time in bringing personalization to its restaurants since the company’s acquisition of Dynamic Yield in March. On the company’s Q1 earnings call this week, CEO Steve Easterbrook said McDonald’s has already installed Dynamic Yield’s technology in 700 McDonald’s locations in the U.S.

As of right now, the technology is being used for drive-thru menus, which it can create based on data like weather, trending menu items, and current restaurant traffic. As you start ordering, the menu will automatically suggest upsell items based on that data as well as what you just ordered. Getting a McCafe? The system is probably going to suggest you order a donut stick, too.

Easterbrook noted on the call that, “Over time using data from the millions of customers that we serve daily the technology will get smarter and smarter through machine learning.”

For now, installments of Dynamic Yield’s tech are in the drive-thru, but Easterbook said we’ll eventually see it across all of McDonald’s digital platforms, including mobile ordering and self-order kiosks.

The focus on drive-thru is in part because of a larger effort by McDonald’s to cut down the amount of time customers spend in drive-thru lines. For most quick-service restaurants, the length of time customers spend in the drive-thru line has gone up over the years, despite advances in technology. Average speed-of-service time for drive thrus was 234 seconds in 2018 compared to 225 seconds the previous year, which only goes to show you it’s not about having the tech but knowing how to use it.

Acquiring Dynamic Yield is one way McDonald’s has addressed this problem. Another effort, which Easterbrook discussed on the call, was an “incentive program,” where McDonald’s locations competed against one another “to deliver the best drive-thru service times.”

However, it’s more likely that an AI-driven platform like Dynamic Yield’s will make the real difference in speeding up service and making it more accurate and personalized — in the drive-thru and otherwise. And Dynamic Yield isn’t the only version of AI up and running. Apprente makes neuroscience-inspired AI that can enable voice-ordering and integrate with restaurant POS systems. And 5thru, which isn’t yet in restaurants but is “coming soon” to a number of large quick-service chains, will (gulp) scan your license plate and offer personalized recommendations based on your linked profile.

McDonald’s installations of Dynamic Yield’s tech seems slightly different, though. Yes, the company is focused on making the drive-thru better, but, as the company plans to expand the tech into other areas, it seems like the Dynamic Yield acquisition is about integrating the many different pieces of the digital restaurant operation to drive the kinds of customer experiences and recommendations that will generate more sales. That could well be the winning strategy for any restaurant in this inescapably tech-driven new age of business.

Presto Raises $30M Growth Round For Its Front of House Restaurant Tech

Presto, whose tech suite helps restaurants organize and manage their front of house, announced today it has raised $30 million in growth funding. The round was led by Recruit Holdings and Romulus Capital, with participation from I2BF Global Ventures, EG Capital, and Brainchild Holdings.

In an interview with The Spoon, Presto founder and CEO Rajat Suri said the new funds will go towards further developing the company’s products, which it expanded earlier this year to include Presto Wearable and Presto A.I., in addition to the company’s tabletop terminal, PrestoPrime.

Suri, who is also the cofounder of Lyft, launched Presto in 2008 after a year spent working in restaurants and prototyping the PrestoPrime based on his observations. The device, which lives on restaurant tabletops, lets guests order, pay, leave feedback and play games while waiting for their food.

If the restaurant is also using Presto’s wearable technology, the terminal can notify a server directly when a guest has a specific need, whether it’s about a soda refill or an undercooked steak.

Those wearables come in the form of an app that’s compatible with any Android device. As Suri points out, wearables like smartwatches make the most sense, since restaurant managers don’t love servers having their phones out and since those devices would be cumbersome anyway during a fast-paced dinner rush. The plus side of having wearable tech sending instant notifications is that it can help a restaurant catch issues as they arise. If that undercooked steak arrives and the guest files a negative comment, it will be able to address the issue in real time, before the customer leaves. If a guest asks for a side of sour cream, the request reaches the server in the form of a digital notification, which is a lot harder to forget and could even help create more accountability, since everything has a digital footprint.

There’s an obvious downside to these real-time updates, though. Guest ratings via technology can affect a server’s bottom line if the manager starts scheduling that person for the slowest shifts due to low ratings. Maybe in some cases that’s justified, but anyone who’s ever worked in a restaurant knows, unhappy customers aren’t necessarily the fault of the server waiting on them.

Suri, of course, has his own restaurant experience, which he’s clearly putting to good use when it comes to how Presto positions these wearables in its array of products. Rather than notify the server (or the GM) about every single activity and issue, Presto Wearable is about important notifications only. “Wearables are meant to cover the biggest gaps, not every gap,” he notes.

And even if an operator wanted their employees to get every last piece of data in the restaurant, that would be impossible for humans to do in any meaningful way. “There’s so much important information coming from various different sources that staff workers can’t make sense of it,” explains Suri of the restaurant operation nowadays. “[Workers] can’t improve on their predictions in a systematic way.” AI, on the other hand, can, and Presto A.I. does the heavy lifting where most of the data is concerned. If it’s Tuesday afternoon, the system can pull weather data or data about external events and make predictions for the Friday night shift. Maybe that college football game around the corner will increase traffic that night. Perhaps bad weather will lessen the number of guests. Presto’s system processes all this data and makes such predictions to help operators better predict and accordingly.

Restaurants who use Presto can pick and choose which of its technologies to use, though as Suri points out, smart restaurants should at this point be making some kind of investment in technology to improve front-of-house operations. “The industry is ripe for change,” he says. “Labor has never had so many options as they do now and the industry has to change because of that to stay relevant. A lot of our partners realize that, and that’s why they’re adopting a lot more solutions.”

Presto currently partners with, according to the company, “five of the top 10 restaurant chains.” Suri wouldn’t go into specific companies (Applebees and Red Lobster are clients), only adding that the company “doubled last year we expect to double again this year in terms of revenues in terms of team size.”

AI Will Now Take Your Breakfast Order at the Drive-Thru

We’ve talked about AI coming to the drive-thru for some time now, and in Denver, CO, one company is finally making that happen. Valyant AI, a CO-based AI company, has set up shop at the Good Times Burger & Frozen Custard restaurant, and its AI platform is taking breakfast orders at the drive-thru.

Valyant AI’s “digital customer service representatives” aren’t all-purpose AI assistants — the company actually built the platform for the quick-service restaurant industry’s many drive-thrus. The patent-pending proprietary platform integrates directly into a restaurant’s drive-thru hardware as well as its POS system.

Better accuracy is something Valyant AI promotes heavily. According to a recent press release, the company, founded in 2017, built and taught the platform using real customer recordings from drive-thrus. And since the system was designed from the ground up for QSRs, it has a significantly smaller range of questions to contend with than a Google Assistant or Alexa. In theory, at least, that should make for more accuracy. The technology also uses the human-in-the-loop model, which is a type of AI that employs both machine and human intelligence to create learning models. So if the system can’t answer a question or fulfill a bizarre order, a human employee can intervene.

More and more, restaurant industry people are calling voice-order tech the next big thing, projecting an explosion of devices and platforms coming to market over the next year or so.

Valyant AI isn’t the first company to try serving up voice control for the drive-thru. Most notably, Clinc, who started out in the financial services sector, is expanding into the QSR realm. Since Clinc’s platform is built to treat everything it hears as data — rather than having to map back to a dictionary — it could potentially handle some of those complex drive-thru orders without the need for human intervention.

According to Valyant AI’s website, the company spent two years developing its technology. And while it’s still in beta, it seems to have launched just in time to seriously compete: 50 percent of revenue for QSR restaurants comes from the drive-thru, according to a recent study, and order accuracy is the number one concern for fast food restaurants in this area.

If Valyant AI’s Denver breakfast run is successful, we’ll probably be holding a lot more conversations with machines when it comes to the drive-thru, at breakfast and beyond.