logo
How Oat Haus is finding success, with its granola butter now on Costco shelves

How Oat Haus is finding success, with its granola butter now on Costco shelves

From the Cleveland Business Journal.
Ali Bonar and her colleagues at Oat Haus used to buy the ingredients for their granola butters at Costco. Now, they are selling their products at Costco.
The food manufacturer debuted its cookie dough granola butter (yes, the butter is made from granola) in an extra-large 27-oz. jar at more than 100 Costco stores in Midwestern states, including Ohio, last week.
"Costco is pretty savage," Bonar told the Cleveland Business Journal. "It's very make-or-break. They'll test you for 12 weeks — they call it a 'rotation' — and especially the first few weeks are the most important. So if we do well in this Midwest region, then they'll start to expand us to other regions."
The Oat Haus entrepreneurs — comprising Bonar, her husband, Eric Katz, and Eric's childhood friend, Ari Schraer — doubled their output to make enough product for the Costco stores.
"We started running a night shift, and Ari started managing that," Bonar said.
"We still make everything from scratch," including toasting and grinding the granola used to make butters in several flavors, such as brownie batter, cinnamon roll and wild berry, she said. "We also fulfill ... all of our e-commerce online orders in-house."
Sign up for Bizwomen's free daily newsletter for news about businesswomen across the country and business intelligence to help you grow your business, advance your career and simplify your professional life.
The brand was born in 2018 out of Bonar's nutrition study at the University of California at Berkley and her eating disorders. While recovering, she added nut butters to her diet but found them hard to digest, so she started experimenting with oats.
Orange background

Try Our AI Features

Explore what Daily8 AI can do for you:

Comments

No comments yet...

Related Articles

John Deere Announces Hundreds of Layoffs
John Deere Announces Hundreds of Layoffs

Newsweek

time2 hours ago

  • Newsweek

John Deere Announces Hundreds of Layoffs

Based on facts, either observed and verified firsthand by the reporter, or reported and verified from knowledgeable sources. Newsweek AI is in beta. Translations may contain inaccuracies—please refer to the original content. Legacy tractor maker John Deere has announced layoffs at three Midwestern facilities as the company grapples with declining sales and the effects of tariffs on its bottom line. "The struggling ag economy continues to impact orders for John Deere equipment," the company said in a media statement regarding the layoffs. "This is a challenging time for many farmers, growers and producers, and directly impacts our business in the near term." Newsweek has contacted John Deere for further comment via email. Why It Matters While the full implications of tariffs on the U.S. economy are still being established by economists and lawmakers, representatives of the agricultural sector have warned that the new duties could increase their costs and threaten footholds in key export markets. As John Deere, whose products are primarily aimed at farmers, said in its announcement, difficulties in the agricultural sector will continue to ripple through its operations and finances. What To Know The layoffs were first disclosed in a series of Worker Adjustment and Retraining Notification (WARN) notices that were later proved to be erroneous. However, John Deere later confirmed that there would be layoffs affecting 238 workers across three factories: Harvester Works in East Moline, Illinois: 115 workers. Last day of work August 29. Seeding and Cylinder in Moline, Illinois: 52 workers. Last day of work September 26. Foundry in Waterloo, Iowa: 71 workers. Last day of work September 19. John Deere said the workforce reductions were due to "decreased demand and lower order volumes." On Friday, the company released its third-quarter results, which showed a 26 percent year-over-year drop in net income to $1.3 billion and a 9 percent decline in net sales and revenues to $12 billion. In a subsequent earnings call, the company attributed many of these difficulties to lower commodity prices and the effects of tariffs. John Deere lawn tractors sit on display outside a Home Depot in Robinson Township, Pennsylvania, on April 11. John Deere lawn tractors sit on display outside a Home Depot in Robinson Township, Pennsylvania, on April 11. Gene J. Puskar/AP Photo "Tariff costs in the quarter were approximately $200 million, which brings us to roughly $300 million in tariff expense year to date based on tariff rates in effect as of today," said Director of Investor Relations Josh Beale. Beale added that John Deere's forecast for the pretax effect of tariffs in the current fiscal year was now "nearly $600 million." This compares to a previous forecast of $500 million. Beale said the "primary drivers" of this increased estimate were increased tariff rates on the European Union and India, as well as the tariffs on steel and aluminum imports that the Trump administration increased to 50 percent on June 4. U.S.-based competitors AGCO and CNH Industrial likewise saw sales weaken during the period, both also pointing to muted industry demand and the effect of tariffs. However, these effects have not proved to be economy-wide. According to recent analysis by Goldman Sachs strategists, cited in Bloomberg, aggregate second-quarter earnings per share for companies in the S&P 500 are up 11 percent from 2024, surpassing expectations of a 4 percent gain. John Deere has unveiled plans that could mitigate some of the tariffs' effects. Earlier this year, the company announced that it would invest $20 billion into its domestic manufacturing capabilities over the next decade. What People Are Saying Josh Beale, the director of investor relations at John Deere, said: "Recent ag policy legislation has been positive and potential developments in trade agreements and demand for renewable fuels could also be supportive. However, until there's more stability in the industry, we'd expect customers to continue to take a measured approach to capital investment." The White House, reacting to the Goldman Sachs report on S&P 500 earnings, said: "Under President Donald J. Trump's bold pro-growth policies, American businesses are thriving like never before—shattering earnings forecasts and propelling the stock market to continued record highs." CFRA Research analyst Jonathan Sakraida, quoted in Reuters, said: "Tariff uncertainty and deflated commodity prices have made farmers increasingly cautious in spending decisions and more hesitant to accept higher machinery prices." What Happens Next John Deere said employees affected by the layoffs were eligible to be recalled to their home factories and would be entitled to receive employment and health care benefits depending on their length of employment.

Ataccama Eyes Data Quality Cracks In AI Infrastructure Desert
Ataccama Eyes Data Quality Cracks In AI Infrastructure Desert

Forbes

timea day ago

  • Forbes

Ataccama Eyes Data Quality Cracks In AI Infrastructure Desert

AI is smart, but only as smart as we make it. This basic de facto industry truth is understood because we know that the intelligence in articificial intelligence directly correlates to its ability to access, ingest and analyze data patterns. Whatever level of semantic reasoning, pattern recognition and non-deterministic agentic intellect a software system aims to achieve is a factor of what data it is fed with. But this is not Costco or Walmart; more does not always mean we're getting a good deal. In the realm of software engineering and data science that services AI, there is also a pressing need to make sure software systems at this level are able to access high quality data. Not wholly dissimilar to the way a good chef fusses over the provenance of their poultry, fish and vegetables, AI data doesn't need to be organic (synthetic data is actually really good eats here), but it does need to have a quality stamp to tell us where it has come from, what its potency is… and indeed what it's shelf life might be. While the current decade has been a cacophony of technology messages related to AI, the watershed moment when agentic AI services start to play a role in working enterprise organizations is just about upon us. Far more sophisticated than chatbots with scripted auto-responses, autonomous systems with an ability to understand intent are starting to edge us towards what could be called generalized functional automation. AI Happened, Infrastructure Didn't Mike McKee, CEO of unified data trust platform company Ataccama thinks that we're at an inflexion point right now. It's a point where AI agents are surfacing inside workflows that are conversational, responsive and fast; but it's also a point where the data infrastructure needed to underpin our new AI services looks a little flaky in places. It's like AI happened, but a commensurate level of intelligent infrastructure didn't. Or at least, not at the same level. Given that McKee (a man who runs a data quality company powered by data lineage, governance and observability functions) would naturally want to advocate an amplified focus on AI data infrastructure, what points does he raise that offer deeper insights here? It's true that only this year have we seen standards like model context protocol and Google's open Agent2Agent protocol arrive; to suggest that the technology industry is working in overdrive to build new connectivity and data management backbone infrastructures for the AI age doesn't perhaps seem too outrageous. As we move on from standalone software bots to multiple AI agents that work together, the impact of poor data in the DNA of one part of a wider networked system starts to sound like more of an issue if it can be shared and reinterpreted elsewhere. 'It's the point where we can mark a new architecture,' said McKee. 'Not one large, centralized model, but many smaller, specialized agents working in coordination. It's the same kind of shift we saw when monolithic mainframe and client-server applications gave way to cloud services, application programming interfaces and the wider birth of cloud-native platforms.' Unified AI Demands Unified Data Going back to the pile-it-high retailers, Walmart is recently reported to have implemented a number of domain-specific 'super agents' for shoppers, employees, suppliers and developers. Each is focused, but together they form what has been called a 'unified AI layer' that runs right across the width and breadth of the organization to move it towards enterprise-wide orchestration of AI agents. With this level of autonomy, the Attacama CEO suggests that the question is no longer 'can we build the agent?' It's 'can we trust it with our data and our decisions?' because when AI agents are exposed to data that is incomplete, outdated or noncompliant, even well-intentioned automation (not that AI normally gets an emotional worthiness grading) could lead to unwanted outcomes. 'This whole backdrop is why the emerging preeminent trend in this space is a pressing need for a smarter data trust layer; one that sits between agents and the data they consume,' asserted McKee. Before an agent runs off to analyze a dataset or recommend an action, it should be able to ask a) is this data accurate and up to date b) is it approved for this use and c) does this action comply with internal company policies and regulatory obligations? That trust check needs to happen in real time, not after the fact, not buried in a quarterly review - and it needs to come with explainability and confidence signals built in, so teams can act on agent output knowing the data is accurate and the reasoning is sound.' This Is Not Governance, This Is Engineering Discussing this subject in a closed press briefing this month, McKee suggests that the industry is now at a point where many enterprises are already treating data trust as an engineering priority, not as a governance task. What that means in practical software and data engineering terms is a set of processes and a platform approach dedicated to embedding quality, lineage policy and compliance signals into the absolute fabric of how agents access and use data. 'Getting this right isn't easy. Orchestrating how agents interact, especially across business units and platforms, is a tough problem… and few companies have cracked it at scale,' explained McKee 'What's emerging is a new discipline: agent architecture. It's about building smarter agents and structuring the environment in which they operate, securely, predictably and accountably. The use of MCP helps standardize how agents connect and exchange context. Teams can ask questions like: 'What should our MCP servers do? How do we design orchestration that's modular, compliant and scalable?' The lesson is that standards matter, as does structure. You end up with cleaner integration and coordination when agents speak a common language… and that's what separates agentic chaos from agentic scale. New Engineering Discipline: Agent Architecture A practical example here could be a team member asking about sales performance and getting a response that explains results, recommends next steps and includes a confidence score, all backed by governed, policy-aligned data. 'Early adopters are already seeing compression in planning cycles and operational workflows. When agents can tap into reliable data, the insight-to-action gap closes dramatically. When humans trust those insights, they're more willing to delegate tasks and make faster, bolder decisions,' said Attacama's McKee. 'It's still early. Most companies don't yet have more than a few agents in production. Many are still experimenting on the edges of the business and that's normal. But the momentum is real. Multi-agent systems are coming and the companies that benefit the most will be the ones that prepare now by standardizing how agents access data, enforcing policies in real time and building the trust infrastructure that lets AI move fast without breaking things. Autonomy and accountability don't have to be at odds, but they do have to be architected together.' The company's most recent products launched to serve this space include the Ataccama ONE data trust platform v16.1. This version introduces data lineage and connectivity capabilities, including enhanced diagram export for audit and compliance use cases and improved lineage visualization tools. It also expands 'pushdown processing' (when computations are moved closer to the data source) for cloud platforms, such as Azure Synapse and Google BigQuery. Competitive Analysis: Augmented Data Quality Vendors This discussion has gravitated around data provenance, data quality and data lineage, but although those terms are all widely accepted measures of worth in the technology industry, analyst house analysis at this level leans on the term 'augmented data quality' vendors. Ataccama holds a respectable enough upper-right quadrant position in this year's market analysis of this space by Gartner, but Qlik is also a frontrunner. After its 2023 acquisition of Talend, Qlik has data quality tools that work alongside a broader set of data integration and data analytics services. Another key player, Informatica is known for its data profiling, cleansing and validation services that reside within the logically named Informatica Data Quality offering that boasts pre-built rules and accelerators. Procurement decisions in the data quality space will now likely be governed by any given platform's ability to exhibit cloud-native alignment, its ability to deliver machine learning and AI-first data quality controls (this stuff used to be a manual process), a technology's ability to handle distributed complex data sets and… crucially, its ability to span data quality, data governance, master data management and (perhaps also) dovetail with business intelligence platforms. There's of course no such thing as a free lunch in data quality i.e. if you want additional automations and wider data validation, data ingest and data enrichment services (something that Experian is well known for in this space), then you do typically have to pay for it. Also on the roster here is IBM and SAS (big vendors, large established enterprise heritage, makes sense), then also DQLabs (strong on AI-powered self-service), Ab Initio, Irion and also Precisely (the result of a Syncsort and Pitney Bowes merger), a company with both data integrity and data quality solutions. Beyond Garbage In, Garbage Out By now, even the layperson understands that artificial intelligence data is a garbage-in-garbage-out conundrum. This is not just a conversation about high-quality data requirements for AI (although it is that too), that's now ground zero tablestakes. We're now moving on to realize that in hybrid multi-cloud environments, distributed data spreads across a whole variety of software systems and tools. Feeding that variegated data stream into AI agents and making sure that the right agent gets the right fuel is (as already stated) a progression onward past compliance and governance (although it is also both those factors too), it is a stage where organizations can 'operationalize automated lineage' into the agentic stream.

McDonald's Once Owned These 2 Popular Fast Food Chains
McDonald's Once Owned These 2 Popular Fast Food Chains

Yahoo

timea day ago

  • Yahoo

McDonald's Once Owned These 2 Popular Fast Food Chains

For a business that's often seen as the gold standard of the fast food world, McDonald's has seen surprisingly little success creating other popular chains. It was big news when the burger restaurant launched its new CosMc's brand back in 2023 because, despite the chain's massive success, it didn't own any other restaurants at the time. Less than two years later, McDonald's has already closed its few CosMc's stores. In this modern chain age, people are used to the idea that chains like Taco Bell and KFC are part of a larger group, but McDonald's stands alone. However, it wasn't always this way. For a brief run in the late '90s and early 2000s, McDonald's was looking to expand its portfolio beyond burgers, and it started with two now-popular chains: Chipotle Mexican Grill and Donatos Pizza. In the late '90s, McDonald's acquired the two chains, buying Donatos in 1999 and investing in Chipotle in 1998. This was part of a larger strategy to expand into other markets, which also included purchasing Boston Market and a stake in the popular sandwich chain Pret A Manger. While it might seem strange now, in the '90s McDonald's was actually kind of flailing. The chain's growth had been slowed by the fact that there just weren't that many places left to open new McDonald's that would be profitable. So, the buying spread that included Chipotle and Donatos was a way to convince investors the company could continue to grow its revenue in other ways. Read more: For Fresh, Not Frozen Fast Food, Try These 12 Popular Chains McDonald's Helped Get A Fledgling Chipotle Off The Ground Chipotle and Donatos were actually in quite different places when each was acquired by McDonald's. Donatos Pizzeria had been founded in Columbus, Ohio, back in 1963 and slowly grew into a major Midwestern pizza spot. When McDonald's bought the brand in 1998, it already had 143 locations. Chipotle, on the other hand, was more of an upstart. Founder Steve Ells had started Chipotle in Denver in 1991, and by '98 it had grown, but to only 16 locations. It was a much less tested brand, and it was McDonald's first ever purchase of an outside concept. It was certainly a gamble. In the end, both chains proved to be mismatches for the burger giant. Chipotle founder Ells was quite protective of the brand and its emphasis on fresh ingredients, rejecting many of the traditional ways McDonald's suggested to expand. Chipotle didn't want to franchise, while McDonald's executives wanted Chipotle to add breakfast and drive-thrus, which Ells also fought against. Still, Chipotle grew like gangbusters with McDonald's investments, expanding to more than 500 locations in less than a decade. In fact, that growth in value actually helped split the companies up. Chipotle remained concerned that it was going to get transformed into a "Mexican McDonald's," and despite the growth, the burrito purveyors were still a small fraction of McDonald's business. It made more sense for McDonald's to cash out and take the profit on its ownership stake, which it did in 2006. Ohio's Donatos Pizzeria Was Never A Match For McDonald's Expansion Plans Despite similar issues in its pairing with McDonald's, Donatos Pizzeria had the opposite experience as Chipotle. Donatos founder Jim Grote had hoped that the partnership would turn his pizza restaurant into a national giant, but his focus on maintaining a high-quality product clashed with McDonald's rapid expansion plans. The burger chain wanted a big return on investment quickly and opened 23 Donatos locations in its first big market expansion in Atlanta. Four years later all those locations had closed. By 2003, McDonald's was worried the focus on expanding other brands was hurting its core business. So, it sold Donatos back to Grote for less money than the chain had originally paid for it. At that point, Donatos had expanded to 182 locations, only a few dozen more than when McDonald's had bought it. Donatos ended up refocusing on what made it unique as a brand, and it now has grown to be one of the most popular pizza chains in the country, with the brand now available at over 450 locations and 179 individual restaurants. The experiments with Chipotle and Donatos show just how important a culture match is when brands buy each other, even when one is as internationally successful as McDonald's. What works for one restaurant chain doesn't automatically work for another, especially when they are targeting different segments of the market. And while it might make other forays into new brands in the future, for now McDonald's has proven to be one of a kind. Read the original article on Tasting Table. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data

DOWNLOAD THE APP

Get Started Now: Download the App

Ready to dive into a world of global content with local flavor? Download Daily8 app today from your preferred app store and start exploring.
app-storeplay-store