Close Ad

The Spoon

Daily news and analysis about the food tech revolution

data analytics

Shiru’s Partnership With Puratos Adds Further Credibility to its Protein Discovery Platform

In the world of food tech, decisions made on viable data are good, and a lot of data is even better. But with Shiru, a functional ingredient discovery company, with a dataset of more than 450 million known proteins, you are in rarified air and a welcome partner to forward-thinking companies.

With that in mind, Shiru has announced a new partnership with Puratos, a Belgium-based company that supplies food ingredients for bakeries. Shiru’s Flourish platform will evaluate naturally occurring proteins that could serve as a next-generation egg replacement.

“At Puratos, we truly believe that collaborations can fuel innovation within the food ecosystem,” stated Paul Baisier, Chief R&D Officer at Puratos. “As a company rooted in biology and science, Shiru is the perfect partner in the Puratos’s journey to finding novel uses for proteins discovered by Shiru’s Flourish platform as functional food ingredients that are sustainable, healthy, and delicious. Together with Shiru, we will be able to accelerate our plant-based product innovation pipeline for the benefit of our customers and consumers.”

Julian Lewis, Shiru’s Vice President of Business Development, told The Spoon why his company is excited about this partnership. “(Puratos) will help us scale up these (egg replacement) proteins using their fermentation facilities to a large kind of food grade sample, where we can then do more extensive food application testing. And through this partnership, we have a clear path to fully scaling these ingredients and bringing them to market.”

At this stage of its life cycle, Alameda, Calif.-based Shiru lives for such a partnership. Its database Flourish Flourish uses AI and machine learning to analyze its database of nearly 450 million proteins found in nature. Each application—for example, a plant-based meat company that wants to add taste to its burgers—identifies ingredients that will solve that specific functional ingredient challenge. This business model, Lewis explains, might expand to his company by commercializing some of its discoveries.

“There’ll be other food categories where we might collaborate, or we might do it ourselves,” Lewis said of opportunities down the road. “We might end up in a hybrid where we’re doing some stuff ourselves and collaborating with experts in other fields just to accelerate its market path.”

Functionality is Shiru’s secret sauce, which is the ability to target a specific property of a particular food product. Lewis explains:

“There are three categories we can play in. There’s replace in which we substitute an ingredient for one that, for example, doesn’t work properly. A second is taste. And what we mean by that is some plant-based foods are not that good, and I have yet to find a vegan cheese that works. Lastly, it is to transform. What new foods could be generated in the future that is not replacing traditional products, which are just new things? And maybe we can do that by discovering new functional protein.”

One of the side benefits of working with a complex database is the ability to help food manufacturers get away from using relatively unhealthy ingredients in some plant-based products that give the impression of being a clean alternative. “We’re aiming to provide a much better toolkit of ingredients to the food developers trying to create plant-based foods,” Lewis said.

Lewis adds that while Shiru is currently generally focused on the plant-based world, there’s no reason it will not be a player as the cultured food business develops. “All food has, I would say, taste and texture challenges, so with cultured meats, some additional ingredients may be required. And we’re already working on the early stage with players in that space as well. Our goal is to create more sustainable food ingredients that are both required and interesting.”

Data: Restaurant Tech’s Biggest Opportunity

This is the web version of our newsletter. Sign up today to get updates on the rapidly changing nature of the food tech industry.

As online ordering becomes more the norm, the next step in on the path to digitization is all about data. More specifically, it is about making sense of the mountains of customer data brought about by the uptick in digital ordering. Think customer order history, dietary preferences, as well as external data like weather, nearby events, and other factors that could impact restaurant traffic.

A company that wants to help restaurants make sense of all this is Brightloom. 

Until relatively recently, Brightloom went by the name Eatsa, and for a time was a restaurant itself, pushing whole hyper-digitized, automated-dining concept long before major QSRs started adopting cubbies and kiosks. The Eatsa restaurant itself didn’t last terribly long. In fact, the company started shuttering these locations in 2017 and by the end of that year was licensing its automated-restaurant technology out to others instead of trying to own the whole stack.

In 2019, rebranded as Brightloom and pivoted sharply away from automated ordering tech to what CEO Adam Brotman refers to as a “data driven personalization service.” Instead of providing cubbies and online order systems for the restaurant front of house, Eatsa now provides a “customer growth” platform through which restaurants can access and analyze their data.

Brotman told me this past spring that the reason for the shift was that digital ordering “was becoming some[thing] of a commodity.” Even before the pandemic shut dining rooms down and forced more restaurants to rely on off-premises channels like pickup and delivery, businesses were incorporating more ways for customers to order digitally. All those order channels — apps, websites, even SMS — produce data that, with the right tools, can be extremely valuable to restaurants in terms of being able to offer customers relevant experiences and upsells.

Boston Consulting Group notes that one-third of restaurants’ digital customers ordered online for the first time during the pandemic. That number is expected to go up, and restaurants will have to meet that demand. “Going digital” nowadays means being able to message and connect with restaurant customers directly, knowing what they buy from how, how often they’re buying it, and through which channels. 

“Even just having a great looking website or mobile app is not easy. Organizing your data and doing data driven, personalized marketing, on your email and push notifications, that is even harder,” Brotman said. 

Brightloom addresses those types of areas for restaurants, and the company has recently seen its popularity among restaurants grow. The company claimed in a press release this month that restaurants using the Brightloom platform “experienced lifts in revenue per guest of 5.7% or more across 23 million guests.” The company has also added larger-name chains, such as Ruby Tuesday and El Pollo Loco, to its roster of customers. Finally, Brightloom also recently launched Brightloom Pro, which includes more customization capabilities for individual restaurant brands. 

Food tech investor Brita Rosenheim recently noted that it’s “dizzying” for restaurant operators to make decisions around how to use their data. Because of that, there is a tremendous opportunity for restaurant tech companies that can partner with these restaurants  to help them “utilize customer data to better uphold their brand, funnel customers into more profitable channels, and make better decisions about merchandising, pricing, and promotions.”

If you want to learn more about this brave new data-centric restaurant world, join The Spoon and guests on August 17 for a virtual Restaurant Tech Summit. Brightloom and Adam Brotman will join the likes of Olo, Delivery Hero, Wow Bao, and many other restaurants and restaurant tech companies. Grab a ticket here, and come ready to ask some questions. 

More Headlines

Bbot Raises $15M Series A for its Restaurant Ordering and Payment Software – The company said it will create new POS and loyalty program integrations with the new funds, and will focus on features for food halls and virtual brands. 

Delivery Service Swiggy Raises $1.25B – The “heavily oversubscribed” round includes the $800 million the India-based delivery service raised earlier this year.

Zenput Raises $27M to Manage Operations for Multi-Unit Restaurants – Multi-unit restaurant operators, grocery stores, and convenience stores can release new operating procedures and health and safety protocols and enforce them across all units. 

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.

Agrylist Raises $8M Series A for Data-Driven Indoor Farming, Rebrands as Artemis

Artemis, formerly known as the indoor-farming company Agrylist, announced today an $8 million Series A round. According to a press release via email, the round was co-led by Astanor Ventures and Talis Capital, with participation from New York State’s Empire State Development Fund and iSelect Fund. The latter two companies are existing investors. The new funding round brings Artemis’ total funding to $11.75 million.

As well as raising new funds, the company has retired the Agrylist moniker and rebranded as Artemis. A new website and new corporate branding are slated for June 2019, according to the press announcement.

Artemis, which was founded in 2015 and is based in Brooklyn, NY, helps indoor farmers better manage the lifecycle of their crops through a proprietary system it refers to as an “enterprise cultivation management platform.” In one interface, which can integrate with existing software tools, the system will help farmers create planting schedules, control the indoor climate, track crop health, detect food-safety issues, and manage labor costs. The system also comes packaged with basic project-management capabilities like to-do lists and daily reminder features.

Increasingly, indoor farmers are turning to these kinds of “one-stop-shop” products to help them make large-scale indoor farming economically feasible and in doing so ensure more consistent production and higher crop yields. Some systems, like those from Liberty Produce and Freight Farms, also bundle hardware like LEDs and the actual grow panels into their end-to-end systems.

Artemis, for now at least, seems more focused on the data aspect of large-scale indoor farming. Gathering useful data on crops and farming operations can help companies not only better monitor crop health, but also help them measure productivity and labor costs, and ensure they’re in line with certain compliance standards.

Even more important, more data could tell us where indoor farming could stand to be more efficient, if indeed it’s efficient at all right now. As Paul P.G. Gauthier explained to me last year, the indoor farming industry tends to claim things that aren’t necessarily backed up by data at the moment. We need more information that can tell us, for example, how much water something like hydroponics actually uses, and where the waste water from those types of operations go.

These are no doubt questions Artemis is tackling, too, as it continues to build out its product. According to the company, the new funding will go towards expanding the Artemis team in product, marketing, engineering, and sales areas, as well as towards scaling sales.

Fourth Helps Restaurants Find and Deal With the Hidden Costs of Doing Delivery

The third-party food delivery market is projected to hit $24.5 billion by 2022. Along with those rising numbers are growing expectations for restaurants to offer off-premises food options through services like Uber Eats and DoorDash.

Those well-tread statements are easy enough to sit and write. But for restaurants, those statements are, to shamelessly quote Jack Sparrow, “just maddeningly unhelpful” if they don’t also include practical advice on how to set up a delivery program and what to look for in terms of financial, operational, and technical surprises.

As a result, says Simon Bocca, COO of restaurant-tech company Fourth, a lot of details can fall through the cracks if a restaurant business doesn’t know to keep an eye on them. Those include things like ensuring the right number of staff is on hand, having extra storage space for all the delivery/takeout packaging, and figuring out how to keep Uber Eats drivers from clogging up the waiting area of the in-house restaurant.

These are the kinds of things Fourth tackles with its restaurant operations platform. The software uses data collection and analytics to help restaurants predict weather patterns, food inventory needs, or how many staffers to put on the floor. If we translate those capabilities to third-party delivery, the system help restaurants know many takeout boxes the business will need in stock, which items are popular as delivery orders, and which days might be more popular for delivery (e.g., game day). Restaurants can know ahead of time to expect an influx of delivery orders and prep as much as possible beforehand.

The data is available through a dashboard interface restaurants can customize to their individual business needs.

All of this goes back to the importance of planning when it comes to delivery. “Planning is everything,” says Bocca. “We’re very much helping organizations make sure they’ve got enough foods from the right vendors at the right price [and] understanding the sales that are going to be coming through those channels they can fulfill.”

It’s even helped restaurants figure out when they need to use ghost kitchens. Bocca calls these kitchens, which have no dining room and are increasingly being used by restaurants to fulfill delivery orders, “a huge opportunity” for traditional restaurants. “That financial modeling and planning the full system is ideal to help organizations [with ghost kitchens]” he said.

At the end of the day, it’s all about what the data tells you. In some cases, it might strongly suggest opening a ghost kitchen. At other times, it will highlight less positive things, like one client Bocca didn’t name who saw a 21 percent jump in delivery orders and a simultaneous drop in in average ticket spend for in-house diners.

“Being aware of the data and the information that can affect your business,” he says. “That’s where we see ourselves as being most valuable for restaurants. We bring in all the data: transaction, productivity, and put that into a really helpful package so that leadership can understand what’s happening.”

Founded in 1999, Fourth serves restaurants in over 60 countries. Bocca said the company would like to replicate its success in Britain here in the U.S. and is currently doubling down on its efforts Stateside. Currently, Fourth counts TGI Fridays, Dairy Queen, and Le Pain Quotidien among its clients.