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The Spoon

Daily news and analysis about the food tech revolution

NotCo

Matias Muchnick Reflects on Being Ten Years Early to the Food AI Party

Back in 2015, when Mattias Muchnick was contemplating the vision for his new company, he imagined building an AI-powered engine that would help big FMCG (fast-moving consumer goods) brands bring products to market faster.

While Muchnick was excited about AI’s potential to transform the food industry, few food executives at the time shared his enthusiasm. Back then, only large tech companies like IBM had experimented with artificial intelligence—such as using Watson’s mainframe-powered AI for food recipe development—but most food industry leaders didn’t view AI as an essential or even necessary tool for creating new products.

Fast forward to 2025, and you’d be hard-pressed to find a food executive who doesn’t recognize AI as strategically critical. This shift undoubtedly explains why Muchnick and his team attracted a packed room last week at Future Food Tech, filled with journalists, investors, startup founders, and major food brands eager to learn about the company’s early success and leadership in food AI.

During the session, Muchnick and his team highlighted their platform’s success (seven out of twenty top FMCG companies have already collaborated with NotCo), discussed the growing trend of food tech startups pivoting toward AI, and shared insights into future developments. Intrigued by the presentation, I decided to follow up with Muchnick afterward to explore these topics further.

In our subsequent interview, I asked Muchnick about the key lessons he’s learned over the past decade, his perspective—as a longtime pioneer in food AI—on the rise of ‘AI-washing,’ and what innovations he’s most excited about moving forward.

You can watch the full interview below:

The Spoon Interviews - Matias Muchnick



The Idea of Food ‘Teleportation’ Isn’t New—But AI Is Finally Making Distributed Digital Food Replication a Reality

Over the past decade, there’s been no shortage of attempts to better understand, map, and recreate food and its properties with the click of a mouse.

From new data ontologies to a proliferation of digital noses, we’ve seen incremental steps toward an envisioned world where the fundamental building blocks of food can be better understood. However, in the past year, there has been a rapid acceleration in our collective ability to digitize various properties of food, largely driven by advances in AI.

The latest example of this comes from Osmo, which recently announced its development of the ability to digitally “teleport” a scent by using AI to digitize and re-materialize it.

Scent Teleportation

As the company’s CEO, Alex Wiltschko, explained:

“We select a scent to teleport and introduce it to a machine called the GCMS (Gas Chromatography-Mass Spectrometry). If it’s a liquid, we inject it directly; if it’s a physical sample, like a plum, we use headspace analysis, trapping the scent in the air around the object and absorbing it through a tube. The GCMS identifies the raw data, which can be interpreted as molecules, and uploads it to a cloud. There, it becomes a coordinate on our Principal Odor Map — a novel, advanced AI-driven tool that can predict what a particular combination of molecules smells like. This formula is sent to one of our Formulation Robots, which treats it as a recipe and mixes different scents to replicate our sample.”

In other words, Osmo breaks down the building blocks (molecules), creates a map, and then sends this digital map to an essence “printer” that re-creates it.

This announcement comes just weeks after leaders of NotCo’s scent and flavor AI team shared research on their new generative AI that creates scent and flavor formulations. Here’s how Aadit Patel, NotCo’s head of product, described the model:

“The system takes your prompt—such as ‘an ocean scent on a breezy summer day on a tropical island’—and creates a novel chemical formulation of that scent in one shot.” The model then generates a corresponding fragrance formula. According to Patel, the model is built on a “natural language to chemical composition” framework, tokenizing molecules to create a system capable of understanding and generating novel combinations.

With years of work focused on digitally understanding, quantifying, mapping, and reproducing scents, flavors, and food building blocks, what is allowing these latest efforts to make such significant leaps?

In a word (or two): AI. In the past, creating a facsimile of a flavor or scent took thousands of hours, relying on trained experts to work in a lab or kitchen, drawing on years of expertise. Now, AI is expediting that process with orders of magnitude more efficiency, often leaving the expert to provide a final sign-off to ensure the AI-created formula meets standards for proximity to the desired result, as well as checks for safety, cost feasibility, and more.

For the record, Osmo isn’t the first company to discuss “teleporting” a formula for digital recreation. In 2018, Japanese startup Open Meals made headlines with its “sushi teleportation” demo, essentially sending 3D printing instructions to create a sushi-like meal. We also saw Cana’s ambitious attempt to make a Star Trek replicator (though, as it turns out, investors weren’t quite ready to enter the food teleportation age).

All of this follows years of efforts to quantify and understand food digitally, including the creation of ontologies for the Internet of Food and early attempts to use AI to analyze food. But over the past couple of years, there’s no doubt that parallel advances in AI (especially in large language models) and breakthroughs in food, olfactory, and chemical science are ushering in a world where true food “teleportation”—or, more accurately, the ability to understand and synthetically recreate food, flavors, and scents—has arrived.

I’m excited to see where this all goes. To manifest the vision laid out in science fiction over the years and imaginative product concepts like that of Open Meal required a true digital understanding of the molecular building blocks of food. With AI, we are closer than ever to that understanding, and the products we’ll see built in the coming decade will not only create some mind-blowing consumer experiences but also possibly fundamentally change how food and beverage products are made and distributed.

Live Event: Using Generative AI to Build Next-Generation Flavors & Fragrances

Join us today for a live event at 8:30 AM PT, featuring the project leads for NotCo’s new AI to develop flavors and fragrances. Register and watch below or head to Streamyard.

Can AI be used to create new flavors and fragrances?

As I wrote last week, food-tech company NotCo has been asking itself this question for the past couple of years. Their answer is a newly unveiled generative AI model, the Generative Aroma Transformer (GAT), that they say is capable of creating new flavor and fragrance formulations.

The company’s Senior vice president of Product, Aadit Patel, described how it works this way: “The system intakes your prompt, such as ‘an ocean scent on a breezy summer day on a tropical island’, to create a novel chemical formulation of that scent in one shot.” From there, the model generates a corresponding fragrance formula. According to Patel, the model is built on a “natural language to chemical composition” framework, tokenizing molecules to create a system capable of understanding and generating novel combinations.

NotCo says early tests have been extremely positive, and the company says their research indicates that GAT’s abilities rival those of human perfumers. At the Food AI Summit last month, the two product leads, Francisco Francisco Clavero and Cindy Sigler, gave an in-depth presentation on the science behind their new model and talked about early results.

Their presentation was fascinating, so I asked them to present to our Food AI Co-Lab community.

Watch the recorded session below:

Using Generative AI to Build Next-Generation Flavors & Fragances

NotCo Has Created A Generative AI for Flavor and Fragrance That Can Create Unique Formulations With Text Prompts

Food-tech company NotCo has developed a novel generative AI model, the Generative Aroma Transformer (GAT), capable of creating new flavor and fragrance formulations. The model, which the company presented on at the Food AI Summit last month, could be a potentially disruptive new tool that could impact a variety of consumer goods markets such as food, personal care, home care, and beauty industries.

The company’s Senior VP of Product, Aadit Patel, talked about the new model in a post on Linkedin today, highlighting how GAT can translate textual prompts into unique chemical formulations. “The system intakes your prompt, such as ‘an ocean scent on a breezy summer day on a tropical island’ to create a novel chemical formulation of that scent in one-shot.” From there, the model generates a corresponding fragrance formula. According to Patel, the model is built on a “natural language to chemical composition” framework, tokenizing molecules to create a system capable of understanding and generating novel combinations.

NotCo says early tests have been extremely positive. Their research indicates that GAT’s abilities rival those of human perfumers. In blind smell tests, fragrances created by GAT proved indistinguishable from those crafted by human experts. This finding signals a potentially significant shift in the industry, where only 600 certified perfumers exist globally.

How it Works

According to research presented at the Food AI Summit, the core of GAT’s functionality lies in its ability to understand and model complex interactions between volatile molecules. The model is trained on an extensive dataset of historical fragrance formulations and the molecular structures of volatile compounds. This training enables GAT to decipher the subtle relationships between different molecules and predict how they will interact to create specific aroma profiles.

The model employs a dual-system transformer network comprising an encoder and a decoder. The encoder processes the user’s prompt (with inputs such as top note (cherry candy), middle note (vanilla) and bottom note (cherry)), capturing the desired aroma profile. This is passed to the decoder, which generates a corresponding sequence of tokens representing the fragrance’s molecular structure.

GAT leverages the atomic structure of volatiles to generate novel formulations. Each molecule is represented as a graph, with atoms described by valence, degree, hydrogen count, hybridization, formal charge, and atomic number. These details are then translated into numerical representations and fed into a Graph Neural Network (GNN) model, which creates a unique vector representing each molecule. Similar vectors indicate similar molecules, allowing GAT to identify and utilize molecular structures with desired aromatic properties.

The potential impact of NotCo’s GAT is substantial. Developing new formulations for flavors and fragrances has traditionally been a time-consuming and resource-intensive process, often requiring weeks or months of expert work. If GAT can achieve the same outcome in mere seconds, it could significantly reduce flavor and fragrance development costs.

If you’d like to learn more about NotCo’s new generative AI tool for developing flavors and fragrances, NotCo’s head of machine learning, Francisco Clavero, and one of their key flavor and fragrance scientists, Cindy Sigler, will be our guests at the next Food AI Co-Lab on October 17th. You can register for this virtual event here.

From Data-Scraping to Discernment Layer: How NotCo’s Giuseppe AI Has Evolved Over the Past Decade

Almost a decade ago, while others experimenting with AI focused on algorithms for trading, diagnostics, or digital advertising, a company called NotCo was experimenting with AI by the name of Giuseppe to create plant-based foods that could match the taste and texture of their animal-based counterparts.

According to Aadit Patel, SVP of AI Product and Engineering at NotCo, the company’s founders (Patel would join a couple of years after the company was founded in 2015) realized early on that, in order to build an AI model that could help create plant-based products mimicking the taste, texture, and functionality of their animal-based counterparts, they would need a whole lot of data.

The problem was, as a startup, they didn’t have any.

When I asked Patel in a recent interview how the company overcame the infamous “cold start” problem—the challenge many embryonic AI models face before they have built large datasets on which to train—he told me they found the solution in a very public place: the U.S. government’s website.

“In the early days, when we had no money, we literally scraped the USDA website,” said Patel. “If you go to the USDA website, there’s a bunch of free data materials for you to use. And I guess no one had actually joined it together to create a comprehensive dataset… So the first versions of Giuseppe were built on that.”

This cobbled-together dataset formed the foundation for Giuseppe’s recommendations, leading to the creation of products like NotMilk, which uses unexpected combinations like pineapple and cabbage to replicate the taste of dairy milk.

As NotCo grew, so did Giuseppe’s capabilities. New analytical labs in San Francisco and Santiago, Chile, gave the company a wealth of new data on which to train its AI. Over time, the model’s ability to create innovative food products also improved.

One of the biggest hurdles in food development is the fragmented nature of the supply chain. Data is scattered across various entities—ingredient suppliers, flavor houses, manufacturers, and research institutions—each holding critical information that contributes to the success of a product. Over time, the company realized that to create an AI capable of building innovative products, it couldn’t rely solely on NotCo’s datasets. Instead, Giuseppe would need to integrate and analyze data from across this complex web of partners.

“What we’ve done with Giuseppe is figure out a way to incentivize this very fragmented ecosystem,” Patel said.

According to Patel, pulling together these disparate datasets from across the product development and supply chain would result in a more holistic understanding of what is needed for a successful product that is better aligned with market realities.

“We realized that if we just made an AI system that’s specific to CPG, we’d be losing out,” said Patel.

Generative AI and Flavor and Fragrance Development

One recent expansion of Giuseppe’s capabilities has been the exploration of new flavors and fragrances using generative AI. While GenAI models like ChatGPT have become infamous for creating sometimes strange and off-putting combinations when designing recipes and new food product formulations, Patel explained that the company has been able to overcome issues with general LLMs by creating what he calls a discernment layer. This layer filters and evaluates the multitude of generated possibilities, narrowing them down to the most promising candidates.

“Discernment is key because it’s not just about generating ideas; it’s about identifying the ones that are likely to succeed in the real world,” Patel said. “With generative AI, you can prompt it however you want and get an infinite amount of answers. The question is, how do we discern which of these 10,000 ideas are the ones most likely to work in a lab setting, a pilot setting, or beyond?”

The discernment layer works by incorporating additional data points and contextual knowledge into the model. For instance, it might consider a formulation’s scalability, cost-effectiveness, or alignment with consumer preferences. This layer also allows human experts to provide feedback and fine-tune the AI’s outputs, creating a process that combines AI’s creativity with the expertise of flavor and fragrance professionals.

Early tests have shown positive results. When tasked with creating a new flavor, both the AI and the human perfumers receive the same brief. When the results are compared in A/B tests, Patel says the outputs of Giuseppe’s generative AI were indistinguishable from those created by human experts.

“What we’ve built is a system where AI and human expertise complement each other,” said Patel. “This gives us the flexibility to create products that are not just theoretically possible but also market-ready.”

CPG Brands Still Have a Long Way to Go With AI-Enhanced Food Creation

Nearly a decade after building an AI model with scraped data from the USDA website, NotCo has evolved its AI to create new products through a collaborative approach that results in a modern generative AI model incorporating inputs from its partners up and down the food value chain. This collaborative approach is being used for internal product development and third-party CPG partners, many of whom Patel said approached the company after they announced their joint venture with Kraft Heinz.

“Ever since our announcement with Kraft Heinz and signing a joint venture, there’s been a lot of inbound interest from a lot of other large CPGs asking ‘What can you do for us?’ and ‘What is Giuseppe?’ They want to see it.”

When I told Patel I thought that big CPG brands have come a long way over the past twelve months in their embrace and planning for using AI, he slightly disagreed. He said that while there’s a lot of interest, most big brands haven’t actually transformed their business to fully create products with the help of AI.

“I would say there’s strong intent to adopt it, but I think there hasn’t been put forth like a concrete action plan to actually develop the first AI-enabled R&D workforce,” said Patel. “There is room, I think, for new AI tech for formulators, and room for best practices and lessons learned of adopting AI.”

You can watch my full interview with Aadit below.

The NotCo team will be at the Food AI Summit talking about their new efforts using generative AI to develop flavor and fragrance, so make sure to get your tickets here.

NotCo's Aadit Patel Talks About the Evolution The Company's Food AI Giuseppe

NotCo Built a Unicorn Using AI To Accelerate Food Innovation. CEO Matias Muchnick Tells The Spoon How They Did It

When Matias Muchnick started NotCo in 2015, food innovation was a slow-moving process.

“Food R&D was three guys in lab coats, doing trial and error in a developmental kitchen,” said Muchnick in a recent interview with The Spoon. “Reading research papers from 1980 about using soy to replace animal-based ingredients. That was it. So whenever you have an industry that has a very obsolete technology, then a lot of bad things happen.”

He and his co-founders wanted to create new plant-based food products, but they wanted to do it in a new way that didn’t rely on antiquated methodologies. Eventually, they started wondering if using technology like artificial intelligence could help them make better decisions and help create new types of food faster.

They decided yes and started building an extensive database of information about all the components that create the taste and experience of food.

“Your machine learning will always be directly proportionate to the amount of data and the dimensions of data that you collect,” said Muchnick. “So from the very beginning, understanding what data was relevant for the objective that we were trying to do, which was replacing animals with plants, was important to us.”

Their database was comprised of chemical and biological attributes that made food what it is and the attributes that impacted the human perception of taste, texture, smell, and color. The goal, said Muchnick, was to create a large enough database of information to use their AI (which would eventually be called Guiseppe) to create a whole line of new plant-based food products.

“We wanted to build a general-purpose artificial intelligence,” he said. “Not an algorithm that is only great at doing mayo, or a burger, or yogurt. The things you’d like from a burger are very different from what you’d like from yogurt, so (we wanted to know) how we could get a real understanding of the human brain to create an algorithm that would attack all of the categories of products. That was super important from the get-go.”

Seven years later, his company is an alt-protein unicorn: the company is growing very fast in the North American alt milk category, just started a joint venture with a food giant, and this week debuted a new alt-meat product at the natural foods show in Los Angeles.

So how does the platform that made all that possible work?

Muchnick gives an example of how the process would work if Giuseppe were used to, say, make a new kind of cheese.

“The algorithm comes up with this crazy amount of recipes and processes attached to each ingredient that we put into it,” said Muchnick. From there, they would take recipes and take them to the “AI test kitchen,” where a group of fifteen chefs try the product out, make the recipe, and then have it evaluated by a trained panel.

“The trained panel gives feedback to the algorithm. Maybe the formulation was good or bad, we feedback the algorithm with the good things and the bad things. So we feedback the algorithm with the many dimensions of the sensory aspects.”

Muchnick says its this continous loop where AI-generated concepts, recipes, and processes are tested in a kitchen, critiqued with feedback, which is then fed back into Guiseppe, which helps NotCo’s AI get better and better.

“You get an algorithm that is working on improving every single day with 1000 recipes.”

But it’s not just recipes getting better, but the optimization of processes around which they run experiments. Muchnick gives the example of a project on frothing plant-based milk. Instead of spending months experimenting with different ways to achieve it, Muchnick says it will help show faster routes to success to help deliver results in a week.

“Instead of starting from scratch with every food formulation you want to create, or any expression you want to create, the AI is telling the food scientist to go through different routes. The algorithm is optimizing every single set of experiments.”

And its this process and the success ultimately drew in Kraft Heinz to consider working with NotCo.

“Kraft Heinz said, guys, you do food products in a quality we’ve never seen before, at a pace we’ve never seen before, and with an agility and an execution that we haven’t seen before,'” said Muchnick. “‘How do you guys do it, and how can we partner up?'”

The answer to that question would eventually be a joint venture.

“They were like, ‘Why don’t we bring superior plant-based products with the familiarity of our brands and with your know-how of executing amazing R&D based products?'”

“And,” said Muchnick, “we’re like, ‘Yeah, I mean, that makes a ton of sense.'”

If you’d like to hear my full conversation with Muchnick about how they are using AI to accelerate food development, just click play below.

Kraft-Heinz and NotCo Form Joint Venture for AI-Powered Food Products

Today Kraft-Heinz and NotCo, the food tech company behind the NotCo brand of plant-based foods, announced they are forming a joint venture to develop a lineup of plant-based food products.

According to the announcement, the new company will leverage the strengths of both companies to develop and bring to market a new line of plant-based products. Called The Kraft Heinz Not Company, it will leverage NotCo’s patented AI platform to develop the food products, while Kraft-Heinz will offer up its production capabilities and formidable sales channels to help bring the products to market.

In joining forces with NotCo, Kraft-Heinz is partnering up with one of the hottest new brands in the fast-growing alt-milk category. The Chilean-based startup has secured distribution deals with a number of premium natural and organic food retailers such as Whole Foods, Sprouts and others since entering the US market in late 2020. The deal also gives the CPG stalwart access to the startup’s patented AI product development platform.

And its this AI platform, which goes by the name Guiseppe, which NotCo cites for its fast success in the US market. Guiseppe works by sifting through huge datasets from the US Department of Agriculture’s (USDA) National Agricultural Library and other sources to find ingredient and processing combinations that would best mimic the elements (flavor, texture, etc.) of real meat or dairy in plant-based analogues. The goal is to find the types of combinations that can create a product that completely mimics traditional meat and dairy — a feat few if any plant-based protein-makers have yet to achieve.

For NotCo, which has seen bigger CPG brands like Danone attempt to mimic its playful and somewhat irreverent product branding, the JV marks a potentially powerful new way to reach a broader swath of consumers in an increasingly crowded alt-protein market. However, by launching an entirely new set of products into the market with Kraft-Heinz, NotCo also runs the risk of potential cannibalization of its existing product lines. The deal announcement doesn’t specify how the new JV will sort out how the two will divvy up retailers, something that could come into play since some retailers will not have shelf space for similar alt-milks from the two brands. It could be that the Kraft Heinz Not Company will focus on new products such as plant-based cream cheese or other categories that play to Kraft’s strengths.

Finally, one wonders if this new JV will set a template for other large CPG brands looking to rejuvenate their product lines as more consumers turn to plant-based diets. Many of the old-school brands are ill-equipped to utilize newer product development tools like AI to create new products, so it makes lots of sense to partner up with ascendant brands well-versed in faster digital-cenric product development methodologies.

I Tried NotCo’s A.I.-Generated Milk Alternative

NotCo, a Chile-based company, is sometimes referred to as the Impossible Foods/Beyond Meat of Latin America. The company produces various alternative products including plant-based mayo, burgers, ice cream, and milk. When they reached out to me recently offering to send samples of their products, I happily accepted.

NotCo uses its patented A.I. platform to determine what plant-based ingredients would best replicate properties found in animal-based ingredients. For example, in its alt-milk products, the two main ingredients are simply water and pea protein. However, there is a small amount of pineapple juice, cabbage juice, sunflower oil, and chicory root, among other natural flavors, added to the milk alternative to bolster its flavor and texture.

The Bezos-backed company sent me a half-gallon of its whole milk and 2 percent milk alternatives. I first poured a small glass of the whole milk and drank it straight up. At first sip, I was surprised by the sweetness of it. Surprisingly, there are only three grams of sugar in a single serving. It had a vanilla flavor with a light aftertaste of coconut. The two percent milk had the same flavor, but tasted a tad bit more watery than the whole milk.

Photo of the alternative whole and two percent milk that was sent to me

I heated some of the whole milk in a pot on the stove and attempted to produce foam with my handheld frother. I had no luck, and the milk was completely flat after frothing for about two minutes. In defense of NotCo, they do not make any claims about their milk’s ability to froth, and I do not have barista-grade equipment. It makes me wonder if the company has plans to develop a “barista version” of its milk that is meant to produce a thick froth for lattes, like many alternative milk companies have done.

NotCo’s whole milk after being heated and frothed

I haven’t had milk for about eight years, but for what it’s worth, NotCo’s NotMilk reminded me of real dairy milk. The sweetness of NotMilk was reminiscent of the sweetness that lactose provides in dairy milk. However, there was something about the NotMilk that still suggested it wasn’t actually dairy. While I couldn’t quite pinpoint the reason, it could very well be the slight aftertaste/mouthfeel of pea protein. Overall, though I would say that NotMilk is a pretty good milk alternative product.

Pea protein, meanwhile, is a popular ingredient in the plant-based space due to its neutral flavor, versatility, and high protein content. It is the second ingredient in NotMilk, and both the whole and 2 percent milk contain four grams of protein in a single serving (one cup). U.S.-based Ripple and Swedish-based Sproud are two other companies that both also produce alternative milk made from pea protein.

NotCo launched its milk in the U.S. last year, and it seems like Oatly will be one of its biggest competitors in the alternative dairy space. Oatly has steadily gained a cult-like following in the U.S. since launching in independent coffee shops in 2016. The Swedish-based company went public this March, and raised $1.4 billion USD through this.

In the U.S., NotCo’s milks are available in stores like Whole Foods, Sprouts, and some independent grocers and retailers. A half-gallon of the alternative whole or two percent milk normally retails for $4.99.

Bezos-Backed NotCo Raises $235M for Plant-Based Alternatives

NotCo, a Chile-based food tech company that produces various plant-based alternative foods, announced today that it has raised $235 million in its Series D round of funding.

The round was led by Tiger Global and saw participation from DFJ Growth Fund and ZOMA Lab, with individuals also joining including Jack Dorsey, Joe Gebbia, Lewis Hamilton, Roger Federer, and DJ Questlove. Existing investors include Bezos Expeditions, EHI, Future Positive, L Catterton, and Kaszek Ventures. This brings the company’s total funding to $360 million.

This new capital will allow NotCo to expand into new product categories in North America and scale its proprietary A.I. platform. Additionally, the funds will help the company accelerate its plans to launch in Europe and Asia. Currently, NotCo offers five products: NotMilk, NotBurger, NotIceCream, and NotMayo. The products are available in approximately 6,000 retailers and foodservice locations throughout the U.S., Chile, Brazil, and Argentina.

Something that sets NotCo apart from other plant-based companies is its use of its A.I. technology (the company has five patents in the U.S. for this). Called Giuseppe, the proprietary A.I. platform analyzes the properties of thousands of plants in a database and then creates unique combinations with the goal of replicating animal ingredients. For example, the ingredients in the NotMilk product include pea protein, pineapple juice, chicory root, coconut oil, and cabbage juice.

NotCo joins the ranks with other large players in the plant-based space that have successfully expanded internationally. Beyond Meat fortified its presence in Europe earlier this year, and around the same time announced that it had opened a manufacturing facility in China. Impossible Foods and Just Eat made major expansions to Asia in the fall of 2020. Oatly is currently building or planning future production facilities in Singapore, China, and the UK.

In the U.S., NotCo’s NotMilk is currently available in Sprouts, Whole Foods, Wegmans, and other retailers. All of the company’s products are available in Chile, Brazil, and Argentina. By the end of 2021, NotCo aims to have its products available in 8,000 retailers globally.

Chile’s NotCo Nabs Investment From Danny Meyer-Affiliated Growth Equity Fund

Alternative protein company NotCo announced this week it has received an investment from from Enlightened Hospitality Investments (EHI), a growth equity fund affiliated with Union Square Hospitality’s Danny Meyer. Financial terms of the investment have not been disclosed at this time, though the company says it has raised “more than $130 million to date” and intends to get to a $1 billion valuation by the end of 2021.  

Chile-based NoCo says the investment will help it further expand in the U.S. and launch new products in the Latin America market. Currently, NotCo sells its pea protein-based milk alternative to U.S. consumers at Whole Foods stores and through online retailers Veji and Imperfect Foods. In Chile, Brazil, and Argentina, the company also has burger, mayonnaise, and ice cream alternatives. 

NotCo puts special emphasis on Giuseppe, the AI platform powering the company’s products. Giuseppe sifts through huge datasets (for example, from the USDA’s National Agricultural Library), to find ingredient and processing combinations that would best mimic the elements (flavor, texture, etc.) of real meat or dairy in plant-based alternatives. The goal is to eventually find flavor and ingredient combinations that can exactly mimic a burger, slice of cheese, or glass of milk.

A handful of other companies also use AI-based methods to create meat and dairy alternatives, from Climax’s cheese to Spoonshot’s platform that identifies novel flavor combinations.   

The EHI involvement will, says NotCo, expand into the U.S. foodservice market. In Latin America, the company already has existing partnerships with Papa John’s and Burger King.

Plant-Based Food Producer NotCo Granted U.S. Patent for Its AI Technology

Chile-based alt protein company NotCo announced today it has been granted a U.S. patent for its artificial intelligence (AI) tech. 

NotCo, sometimes referred to as the Impossible or Beyond of Latin America, first launched its plant-based milk alternative, NotMilk, in the U.S. at the end of 2020. The company said it will soon open an office in NYC, and already has offices in San Francisco. The company also has U.S.-based retail deals with Sprouts, Wegmans, and online grocer Imperfect Foods.

The company makes a plant-based milk from pea protein. In Latin America, it also sells a plant-based mayo, a burger-like item, and ice cream in Brazil, Chile, and Argentina.

The company’s AI platform, named Giuseppe, sifts through huge datasets (for example, from the U.S. Department of Agriculture’s (USDA) National Agricultural Library), to find ingredient and processing combinations that would best mimic the elements (flavor, texture, etc.) of real meat or dairy in plant-based analogues. The idea, of course, is to find the types of combinations that can create a product that completely mimics traditional meat and dairy — a feat few if any plant-based protein-makers have yet to achieve.

From the patent:

A formula generator learns from open source and proprietary databases of ingredients and recipes. The formula generator is trained using features of the ingredients and using recipes. Given a target food item, the formula generator determines a formula that matches the given target food item and a score for the formula. The formula generator may generate numerous formulas that match the given target food item and may select an optimal formula from the generated formulas based on score.

NotCo said in today’s press release that the AI platform also looks for “unexpected plant combinations” that could also achieve the desired taste and texture of the real thing. 

NotCo is not alone in this AI-based approach to plant-based proteins. Climax is another notable company in this space, and is currently crunching data sets to find the most appropriate ingredient combinations for plant-based cheese. In the wider food industry, Spoonshot uses AI to identify novel flavor combinations, and Brightseed leverages the tech to uncover phytonutrients in plants.

The company has raised a total of $120 million so far from a pool of investors that includes Jeff Bezos, Kaszek Ventures, and Maya Capital. It is also expanding to further international locations, including Colombia, Mexico, and Canada.

NotCo’s Milk Alternative Launches in the U.S. Today

Chile-based food tech company NotCo, best known for its alt-protein products, announced today it is launching its plant-based milk alternative in Whole Foods stores across the U.S. These alt-milk products will be available in Whole and Reduced Fat varieties at stores as of today, according to a press release from NotCo sent to The Spoon.

The launch follows an $85 million Series C funding round from September, an investment the company said would help NotCo expand internationally through both retail and restaurant partnerships. Previously, NotCo raised $30 million in 2019 from a pool of investors that included Amazon head honcho Jeff Bezos. The company’s total funding to date is $115 million.

All that funding and expansion aside, the company has undergone some changes in the last year, including having to restructure its business and do layoffs. NotCo also shut down its Santiago-based production plant for its NotMayo product, passing production of that item on to an unnamed third party. Currently, the NotCo website only lists the milk alternative under its products. A spokesperson for the company told The Spoon that the milk products are currently for sale across Latin America.

Like other milk alternatives out there, NotMilk is comprised of a number of plant sources, pea protein being the main one. Pineapple juice concentrate, cabbage juice concentrate, and chicory root fiber are others. NotCo claims that, with these ingredients as well as AI and machine-learning algorithms, it is able to recreate the taste, texture, and mouthfeel of regular ol’ milk. As of today, U.S. consumers can see how it stacks up in terms of those elements to other choices already in grocery stores.

On that note, NotCo joins the likes of Oatly, Take Two, and others that already have plant-based milk alternatives in the U.S. market. Further down the line, all these companies may have to compete with Impossible, which is developing its own milk alternative. Whether other alt-protein heavyweights like Perfect Day and Eat Just ever come to market with milk alternatives remains to be seen. Regardless, NotCo faces more than a little healthy competition when it comes to retail shelves in the U.S.