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The Spoon

Daily news and analysis about the food tech revolution

AI

Google Cloud Team Uses AI to Develop Hybrid “Cakie” and “Breakie” Recipes

I remember at some point last year after the pandemic had started, I called three different grocery stores to check if they had yeast in stock. Of course, I had no luck at any location because our country had entered a baking extravaganza; ovens were fired up non-stop across the country to bake sourdough, banana bread, and chocolate chip cookies. Due to this baking frenzy that overtook our country in 2020, the Google Cloud team analyzed the ingredients and specific ratios used in favorite baked goods like cookies, bread, and cakes through the use of a machine learning program. The goal was to essentially determine what defined something as a cookie, cake, bread, and how each of these categories differ from each other. As a result, the team also used its AI to produced two new hybrid baked goods called a “Cakie” and a “Breakie”.

The team collected hundreds of different recipes for bread, cookies, and cakes to create a dataset and then used Google Cloud’s AutoML Tables tool to build a machine learning model. The model analyzed the amounts of key ingredients such as yeast, butter, eggs, and sugar, in the different recipes and was able to determine if the recipe was for bread, cake, or cookies. Bread recipes were accurately labeled about 90 percent of the time, but sometimes the model mislabeled cookie recipes for cake recipes.

Using this model, the team used ingredients and measurements that the algorithm determined were key for cookie, bread, and cake recipes to create two new hybrid recipes. The “Breakie” is half cookie and half bread, which turned out to be a fluffy, airy cookie with a texture similar to that of a muffin. The “Cakie” maintained a cake shape and sponginess but has a buttery flavor and crispy outside like a cookie.

Google Cloud’s project is just one neat example of how machine learning and AI (artificial intelligence) can be applied in food technology. Spoonshot recently launched a free version of its AI flavor-pairing tool that offers the user both novel and unique pairings for different ingredients. Brightseed created an AI platform called Forager, which detects hidden phytonutrients in different plants. Even companies like Starbucks, Sweetgreen, KFC, and McDonald’s use AI to some degree in order to streamline day to day operations.

As we still find ourselves in the pandemic early 2021, you may be looking for some new recipes to bake. You’re in luck; the Google Cloud team provided the two recipes created through the use of AI, the “Cakie” and the “Breakie”.

Spoonshot Launches Free Version of its AI-Based Flavor Pairing Tool

Spoonshot, which uses artificial intelligence (AI) to uncover novel flavor combinations, has launched a free version of its tool that is accessible to anyone. Spoonshot CEO and Co-Founder, Kishan Vasani spoke about the new level of service at The Spoon’s Food Tech Live event earlier this week.

Up to this point, Spoonshot’s platform has been a B2B play, meant for CPG companies and foodservice operators looking ahead to see what the next food and flavor trends might be. As we wrote last year when Spoonshot raised $11 million:

To get ahead of the curve, Spoonshot’s platform examines data from across a vast number of food-related sources including online menus, food science, CPG ingredients and online food communities. Spoonshot runs this data through its proprietary machine learning and AI algorithms to help companies identify existing and novel flavor combinations.

The key word here is “novel.” When you enter a flavor like “banana” into Spoonshot’s Ingredient Network tool, the service brings back a number of potential flavor combinations and scores them based on novelty. Combining banana + chocolate is common, but combining banana + aloveera juice is something that probably hasn’t occurred to most people and a combo that Spoonshot says will be tasty.

Armed with this novel combination, a restaurant or CPG company could go about building a new product that will appeal to consumers.

Launched at the start of the new year, Spoonshot’s new free tier of service now allows anyone to try its AI platform out (pricing starts at $99/month). Chances are good that most of us in our everyday lives don’t need enterprise-grade artificial intelligence to uncover novel flavor combinations. But aside from being a fun (and free) little distraction for everyday chefs, it could also be useful for small CPG or restaurant owners that don’t have R&D budgets to expand their offerings.

Brightseed’s First Major Phytonutrient Discovery Finds Black Pepper May Help with Fatty Liver

Brightseed, which uses artificial intelligence (AI) to uncover previously hidden phytonutrients in plants, today announced preclinical data from its first major discovery targeting liver and metabolic health.

The discovery was made with Forager, Brightseed’s AI platform that looks at plants on a molecular level to identify novel phytonutrient compounds (for example, antioxidants in blueberries). Once found, Forager then catalogs these compounds and uses that information to predict the health benefits of those compounds.

With today’s announcement, Brightseed’s Forager has identified phytonutrients that can help with fat accumulation in the pancreas and liver, a condition linked to obesity. Brightseed explained its findings in a press release, writing:

Using a computational approach with data from Brightseed’s plant compound library, Forager identified two natural compounds with promising bioactive function, N-trans caffeoyltyramine (NTC) and N-trans-feruloyltyramine (NTF). Researchers determined that these compounds acted through a novel biological mechanism governing the accumulation and clearance of liver fat. The preclinical data was presented in the fall of 2020 as a poster session at The Liver Meeting® Digital Experience hosted by American Association for the Study of Liver Diseases, and published as abstract #1679 in Hepatology: Vol 72, No S1. 

The release continued:

IIn preclinical studies, NTC and NTF acted as potent HNF4a activators, promoting fat clearance from the steatotic livers of mice fed a high fat diet, by inducing lipophagy.  HNF4a is a central metabolic regulator that is impaired by elevated levels of fat in the bloodstream resulting from chronic overeating. Administered in proper doses, NTC and NTF restored proper function of this central metabolic regulator, including maintaining healthy lipid and sugar levels in the bloodstream to normalize organ function. Their activities were confirmed using a cell-based human insulin promoter activation assay. Forager found NTC and NTF in over 80 common edible plant sources. 

One of those plant sources, Brightseed Co-Founder and CEO, Jim Flatt told me by phone this week, is black pepper. Now, before you run out and grab your pepper grinder, there is still a lot of work that remains before the results of this discovery bear out.

First, the compounds still need to go through clinical trials to validate Brightseed’s initial findings. This includes not only confirming any health benefits, but also determining the doses and best methods for administering the compounds. Then the best plant source for those compounds needs to be determined as well as the best method for compound extraction. Flatt told me that if all goes well, you can expect to see some form of supplement on the market by the end of 2022.

Even though that is a ways off, part of the reason to be excited by today’s announcement is because of how little time it took Brightseed to make this particular discovery. Through its computational processes, Flatt told me his company was able to shrink what used to take years down to months. “Fifteen to 20 percent of time that is computational saves us 80 percent of the time in the lab,” Flatt said.

Brightseed has already analyzed roughly 700,000 compounds in the plant world for health properties and says it’s on track to surpass 10 million by 2025. Doing so could help unlock a number of previously unknown treatments for a number of ailments and conditions as well as general improvement to our metabolic and immuno health.

In addition to independent research such as today’s findings, Brightseed also partners with major CPG brands to help them identify new applications for their products. For instance, Danone is using Brightseed’s technology to help find new health benefits of soy.

Brightseed’s announcement today also reinforces the bigger role AI will play in our food system. AI and machine learning is being used to do everything from turning data into cheese, to solving complex issues around protein folding.

As more discoveries using AI are made, more investment will be poured into the space, which will accelerate even more discoveries.

Sony AI Unveils Trio of Food Projects Including AI-Powered Recipes and Robots

Sony AI, an artificial intelligence and robotics research and development company spun out from Sony, today announced its “Gastronomy Flagship Project.” The new food-related endeavors include an AI-powered recipe creation app, a robot chef’s assistant and a community co-creation initiative.

Sony launched Sony AI last November and the unit became its own company in April of this year. Sony AI’s mission is to develop AI-related products for videogames, imaging and sensing, and gastronomy “with the aim of enhancing the creativity and techniques of chefs around the world,” according to today’s press announcement.

To that end, the three projects Sony AI announced today are:

  • AI-Powered Recipe Creation App – Gathering up data such as aroma, flavor, molecular structure, nutrients and more, this app will use AI algorithms to help chefs create novel food pairing, recipes and menus.
  • Chef Assisting Cooking Robot – This is pretty self-explanatory. Sony AI will develop a robot that can mimic the physical actions of a human chef to do everything from preparing to plating dishes.
  • Community Co-creation Initiative – This project is a little more vague, with Sony AI only saying it will “aim to contribute to the long-term sustainability of the community” through relationships with universities, research institutes, companies and more. The first step in this process is a “Chef Interview” video series on the Sony AI website.

Sony AI’s press release didn’t provide specific details around when or where these projects would launch (other than the video series).

But today’s announcement continues a trend we’ve been seeing with large electronics corporations doing advanced work on food robotics and AI. LG is working on robot waiters with Woowa Brothers in South Korea. And Panasonic is working with with the Haidilao chain of hot pot restaurants in China to develop a robotic kitchen.

Sony itself is no slouch when it comes to food-related robots. The company collaborated on cooking robot research with Carnegie-Mellon University a couple years back. And it has a pretty grand vision for advanced AI-powered cooking robots and assistants that could make Michelin star meals in your home.

How Alphabet’s AI-Powered Leap in Protein Structure Prediction Could Accelerate New Food Development

This past week Alphabet, the parent company of Google, announced that its DeepMind group has solved a long-standing grand challenge in the scientific community around protein structure prediction.

In 1972, scientist Christian Anfinsen predicted that a protein’s structure could be determined by its amino acid sequence. However, figuring out that sequence is immensely difficult since there are near-infinite ways in which a protein can fold. This led Cyrus Levinthal to postulate that calculating all the known configurations would take longer than the age of the known universe if our only way there was brute force calculation (a problem often referred to as Levinthal’s Paradox).

Thankfully, now we won’t have to wait forever (literally) since DeepMind’s DeepFold AI can predict protein structure to the width of an atom within days. My former Gigaom colleague, Darrell Etherington, explained the feat in a post on Techcrunch:

The test that AlphaFold passed essentially shows that the AI can correctly figure out, to a very high degree of accuracy (accurate to within the width of an atom, in fact), the structure of proteins in just days — a very complex task that is crucial to figuring out how diseases can be best treated, as well as solving other big problems like working out how best to break down ecologically dangerous material like toxic waste.

As many in the world of future food development know, AI is becoming an increasingly important tool to accelerate the development of new proteins and other food-building blocks. Companies like Climax Foods are embracing machine learning to help them develop new approaches to making food like plant-based cheese. And now, with DeepMind’s advances in protein folding prediction paths, we can expect AI to become an even more important tool in new food development.

To get a feel for what the impact of this milestone might be, I asked Sudeep Agarwala, a synthetic microbiologist for Gingko, what he thought DeepMind’s work could possibly mean for food:

“There’s so much that can happen with this,” Agarwala told me. “Think about different textures or mouth feels with food proteins that the AI can design. Or even different amino acid contents for the proteins. And that’s just for the end proteins (if you want the protein as the end product).”

“If we’re engineering proteins inside the cell to produce a small molecule (a flavor, a fragrance, a fat) we can think about making completely new enzymes with multiple functionalities. So something like a 5-step process might be condensed. This has the potential to simplify so much of the metabolic engineering we do to produce these products.”

Agarwala also believes that using technology like that of DeepMind’s will also be way more effecient in terms of resources and have a much smaller ecological footprint:

“Even if the proteins aren’t ultimately going to be consumed, being able to rationally design enzymes that are involved in producing small molecules (think fats, fragrances, or flavors) is really exciting to think about,” said Agarwala. “This isn’t small potatoes: think about how much energy and resources goes into making vanilla or saffron, for example. Being able to do this more efficiently will provide a less ecologically expensive way to produce these materials.”

Just as disease and virology experts are buzzing about what this new milestone could mean for their work, it’s clear food scientists like Agarwala are equally as excited.

“It’s such an exciting time to be a biologist!” said Agarwala.

Yes, it is.

Kea Raises $10M to Bring its AI-Based Phone System to More Restaurants

Kea, which makes an AI-powered virtual phone assistant for restaurants, has raised a $10 million Series A round of funding. TechCrunch was first to report on the round, which was led by Marbruck, with Streamlined Ventures, Xfund, Heartland Ventures, DEEPCORE, Barrel Ventures and AVG Funds, as well as other angel investors participating. This brings the total amount raised by Kea to $17 million.

Basically, Kea is building an automated way for restaurants to answer the phone. The natural language processing software can hold a “conversation” with a customer to take and process their order. You can hear a demo of how it works on the Kea website.

As TechCrunch points out, many restaurants are understaffed and don’t always have a dedicated person to work the phones and take orders. During this time of off-premises eating, not answering the phone can translate into a lot of lost business. Plus, ordering directly from the restaurant instead of through a third-party like DoorDash or Uber Eats helps the restaurant avoid the sky-high commissions those services charge.

Kea is among a wave of natural language customer interaction systems coming to market. Google famously made news a couple years back with its Duplex AI-powered voice assistant for consumers to make automated restaurant reservations that sounded almost too human. Google also developed the CallJoy virtual phone assistant for small business owners. Clinc’s technology brings natural language conversations to the drive-thru, while McDonald’s acquired Apprente last year to add more voice capabilities to its drive-thru.

Kea told TechCrunch that its service is currently live in more than 250 restaurants including Papa John’s. With its new cash, the company is looking to be in 1,000 restaurants in 37 states next year.

SKS 2020: Grabango Says its Computer Vision Avoids Biases Because it Doesn’t Use Facial Recognition

Grabango CEO Will Glaser said this week that because his company’s technology doesn’t use facial recognition, it can avoid some of the same types of racial biases found in other computer vision and machine learning algorithms. Glaser’s comments came during my fireside chat with him at our Smart Kitchen Summit this week.

Grabango makes cashierless (or as Grabango calls it, “lineless”) checkout technology for grocery retail. Grabango installs hundreds of cameras in the ceiling of a store, and through a combination of computer vision and AI, keeps track of what people put in their basket (or pocket). When shoppers go to check out, they choose a cashier or use an accompanying mobile app that automatically charges for the items kept.

This type of computer vision-based system is also used by other cashierless checkout companies such as Zippin, Standard Cognition and Amazon, and could become more widespread as demand for contactless retail experiences grows.

But the problem with some computer vision + AI systems is that their algorithms can contain the human biases of their creators. As TechCrunch wrote earlier this year, “MIT researchers reported in January 2019 that facial recognition software is less accurate in identifying humans with darker pigmentation.” In a busy retail environment where a lot of people are grabbing a lot of different items all throughout the day, it’s not hard to see how this type of bias could adversely impact people of color.

Glaser recognized some of the problems that can arise when algorithms are only developed or trained on white men in a lab. He made a point of saying that Grabango’s technology does not use facial recognition, and that it has a 99.6 percent revenue accuracy rate. Grabango’s system does follow a person around store, but doesn’t personally identify them (it can, however, stay with a person even after they have put on or taken off a jacket).

Grabango’s system is also being used in real world conditions, so it continues to train its algorithms in a diverse setting. Last month, Grabango announced that it’s integration with Giant Eagle’s GetGo Market in the Pittsburgh area went live. So now we’ll be able to see in a more open environment if Glaser’s claims hold true.

The Food Tech Show: Did The Automat Ever Really Go Away?

In this week’s episode of The Food Tech Show, we talk about those new contactless systems and compare them to a technology from long ago: the automat.

Yep, that old-school idea born in New York City a century ago is back (or maybe it never left?), showing up everywhere from restaurants to condos.

Jenn Marston waxes nostalgic about the automat and other concepts that seem to be getting a second look as the food system looks to reinvent itself in the wake of COVID-19.

We also talk about these stories in today’s podcast:

As always, you can listen to this week’s podcast on Apple Podcasts, Spotify or wherever you get podcasts. You can also download the episode direct to your device or just click play below.

Birdie Uses AI to Scour Reviews and Help Brands Understand Their Products

Thanks to our connected world, people who either love or hate a product, don’t have to keep their opinions to themselves. There is no shortage of platforms to express their thoughts.

This steady stream of opining is actually a source of fuel for Birdie, a company that uses AI to comb through product reviews and discussion forums (written in English) to surface product insights for CPGs and other other product brands.

For instance, by applying its AI to customer reviews of V8 juice, Birdie was able to show that people were often using the vegetable drink as a hangover remedy. By uncovering this data, V8 could then choose to create a specific line of drinks or marketing campaign that reaches this particular type of indulgent adult. The same idea applies to those pouches of pureed foods for toddlers. Birdie discovered that athletes and outdoors people carried these with them because they were easy to carry and loaded with nutrition.

Birdie is not a social media listening tool. It’s not just tallying up social mentions of a brand and analyzing timelines to see what is trending. Instead, the company is more focused on consumer product reviews on Amazon, Google and other places where purchases can be verified and are filled with more details about how the product arrived, how it was used, how long it lasted, etc.

“Our main differentiator is the fact that we chose to be very focused on products, and built a deep dictionary that relates to the buying journey of consumer products,” Patrícia Osório, CMO of Birdie told me by phone this week. “We capture the data related to a product attribute, or usage of the product, how they bought the product. With that, we can show our clients a detailed and easy to find view about how consumers are interacting with their brand.”

According to Osório, the number of product reviews in the U.S. has been growing quickly, with an increase of 60 percent year over year. She said there is an average of 621 new reviews written per day on food products, with an average of 342 reviews per SKU.

In a way, Birdie is like a distant cousin to Spoonshot, which applies its AI to vast datasets on food to uncover novel flavor combinations. Only in Birdie’s case it is uncovering novel uses for existing products.

Founded two and a half years ago, Birdie has raised $1.6 million in Seed funding and counts Procter & Gamble among its clients.

Birdie’s technology actually fits in with the larger hacker culture that we live in today. In addition to expressing their opinions, people love deconstructing and re-purposing existing products to fit their own needs, and sharing their findings with other people online. All this adds up to a never-ending source of data for Birdie’s algorithms, and more product insights for brands.

Plant Jammer Gets €4M Investment for its AI-Powered Recipe Platform

Plant Jammer, a four-year-old Danish startup building an AI-powered cooking assistant, is one step closer to its goal of reaching one billion people, thanks to a €4 million investment in its AI recipe algorithm and platform. The Copenhagen-based company plans to expand its presence in the digital food space by licensing its API to third parties who can build branded customized experiences for their customers.

The new injection of capital comes from Danish investment firm Vaekstfonden, German food processing company Dr. Oetker, and German appliance manufacturer Miele. Miele had previously invested in Plant Jammer in 2018.

”Plant Jammer’s combination of recipe creation with AI is both unique and functional. We expect that this technology will be a core pillar in the connected kitchen of the future. Therefore, we believe Plant Jammer has great business potential,” says Dr. Christian Zangs, Managing Director of Miele Venture Capital.

Plant Jammer’s application, already in use by 10,000 households in Europe, allows users to build customized recipes by factoring in their individual preferences and what they may have in their home or what may be on sale in the local supermarket. While the app is focused on plant-based and vegetarian creations, partners who license the platform are not limited to those options. The database also contains food choices that include animal products and dairy; the PlantJammer app chose not to surface those results allowing the company to focus its version on a select niche.

In an interview with The Spoon, CEO and founder Michael Haase explained that partners who license the Plant Jammer’s API will pay based on the number of “calls” or accesses by users. For example, a grocery chain in Sweden can use the Plant Jammer API to develop a branded application such as a chatbox, that could include such extras as a link to online shopping. Each time a user of that third-party application builds a recipe, based on ingredients, tastes, diet, or any number of factors, the PlantJammer AI-driven database would work behind the scenes to deliver the results.

“I like to think of the analogy of the gold rush,” Haase adds. “We are interested in being the supplier of the jeans and shovels that enable others to do their jobs better.”

Personalized data from commercial partners will not be shared with Plant Jammer, but those partners can pass on generalized information via tags to allow the Haase’s company to continue to innovate on its platform. There are several areas Haase hopes to develop focused around food waste and the increased use of the excess capacity of local farmers and vendors.
Initially, the company founder says, the goal is to focus on food waste in the home. Haase says that 50% of all food waste takes place in the home, so we want people to build recipes based on what they already have in their refrigerator or cupboard.

“Our declared purpose is to empower one billion people with food habits that increase their health and the health of the planet,” Haase added.

That said, Haase admits his goal is a lofty one. “Right now, we are in a world of what I would call ‘trickle-down gastronomy’,” he says. “There is a huge divide between those whose world is focused on things such as molecular gastronomy and the masses. If we can show people that you can make something great in 25 minutes with simple ingredients, that would be great.”

QCify Goes 3D for Quality Control and Fair Pricing in the Food Supply Chain

As the economy is barraged daily by some kind of pandemic-related bad news, many businesses remain closed (or serving far fewer customers), job losses continue to pile up and people all over are being more cautious about how much they spend.

The food supply chain is not immune from this belt-tightenting. Looking to save some money, food buyers may haggle more vigorously over what they pay per pound for something like almonds, costing the growers and processors money. This problem, Raf Peeters told me, is where QCify can help.

Peeters is the CEO of QCify (pronounced kew-sih-fye), which uses a combination of computer vision and artificial intelligence (AI) to perform quality control on food items at processing plants. Right now QCify inspects almonds and pistachios by running samples through a special machine that uses six cameras to capture a 3D image of each almond. The company’s AI then analyzes the image and grades the almonds based on USDA (or other) criteria such as size, color, insect damage, imperfections, etc.

All that data collected by the inspection machines are sent back to to QCify HQ, where it is incorporated into the company’s algorithms. Twice a year QCify then sends out updates to all of its installed machines, which means that even if a customer bought a QCify system a couple years ago, it will run the newest AI. “Customers feel like they have the latest and greatest,” Peeters told me by phone this week.

Qcify explainer video

The result of all this computer vision and machine learning is that nut processors can set a fair price for their wares, based on objective criteria (like the USDA grading). Right now, QCify works with almonds and pistachios, and has customers in both the U.S. and Australia. A buyer can’t argue over the quality of the almonds (and thereby demand a lower price) because the processor not only has the grade from the QCify system, but it can also produce the sample images to show exactly what quality the almonds or pistachios are in.

QCify isn’t the only company looking to remove biases from the food supply chain using computer vision and AI. AgShift and Intello Labs do much the same thing. Peeters said that QCify is different from the competition because its six-camera setup captures 3D images of the nuts, instead of just scanning the top an bottom of the food, which Peeters claims is what his competition does.

QCify was founded in 2015 and Peeters said they company has only raised an unspecified amount of angel investment money. The company sells the machines themselves and charges a monthly/annual subscription fee for updates and calibration. While he wouldn’t reveal pricing, Peeters said that customers can earn their money back within a year.

In these cash-strapped times, a faster ROI isn’t just peanuts, which, coincidentally is one of the next nut categories QCify is expanding into.

KloveChef Opens Up Voice-Guided Cooking Platform to Publishers

KloveChef, the voice-guided cooking startup cofounded by one of India’s biggest celebrity chefs in Sanjeev Kapoor, is opening up its platform this month to publishers wanting to add voice-guided cooking functionality to their recipes.

The new tool will allow anyone who has recipe content — chefs, cookbook authors, bloggers or food retailers — to upload their recipes to KloveChef’s platform via a web interface and it will convert them into a voice-guided recipes.

“We will democratize the interactive recipe creation and distribution,” said Bahubali Shété, KloveChef cofounder and CEO, in an interview with The Spoon.

Shété told me that recipe publishers will be able to use KloveChef to publish their recipes across a variety of voice platforms such as Amazon Alexa, Google Assistant, Google Home and Amazon Fire TV. To do so, they just copy the recipe URL or paste the full recipe into the web interface and KloveChef will convert into a voice-guided recipe.

Shété also said that publishers will have the option of letting users send their recipes posted on other web channels such as YouTube or Pinterest to their voice assistants for guided cooking.

KloveChef is opening up their voice platform after finding some success with their Alexa voice skill targeted primarily at home cooks in India. According to Shété, the guided cooking assistant has a total of 465,000 users and 100,000 monthly active users.

Shété says publishers can make money through KloveChef if the recipe is converted into a shopping list. The recipe-to-shopping list feature, which KloveChef has been testing through its app in India, currently has over 1 million recipes converted into shopping lists via voice search.

I have to admit, I like the idea of self-publishing recipes to voice platforms. It reminds me of the early days of ebooks, when authors would use technology from early pioneers like Smashwords to put their books into the world and on other popular platforms. Perhaps not all that surprisingly, just as like those early days of ebooks, recipe self-publishers are relying on Amazon to reach the end consumer, only instead of Kindle this time it’s Alexa.

It’s too soon to see how successful KloveChef will be in attracting cooks for its voice guided recipe assistant outside of India. In its home market, they’ve been able to leverage the large reach of Kapoor, while here in the states, Alexa tends to favor its featured partners such as Food Network or Tasty. KloveChef will have to compete with the algorithm-favored partners through attracting recipe publishers such as popular food bloggers or food retailers with built-in audiences to accrue a sizeable user base.

Looking forward, the company hopes to also attract users by making the platform better over time. One of the early features will be adapting guided cooking where users can speed up a recipe or slow it down depending on their experience. The company plans to release the new capabilities by mid-August.