Most factories and sourcing teams know AI will be an inescapable part of the future and are experimenting with its uses within the confines of their existing businesses—but very few have reached the stage of driving it forward with a real strategy and real results. In 2026, many still see AI as just another cost, or feel that they don’t have the right data or a strong enough technical team to implement it.
That’s why Daren Hull, CEO of AI-driven end-to-end product creation platform CreateOne, knew something was different about the efforts of a recent client, Grupo Beta, the textile and apparel manufacturing conglomerate based in Honduras, which recently implemented the tool into its operations.
The numbers for Grupo Beta’s use of CreateOne were staggering to Hull: over 1,600 questions answered, more than 100 applications generated, and an adoption rate that left even seasoned tech veterans in awe. “We’ve never seen a client pick up at this pace,” Hull said. “Everybody fears AI is coming, they want AI agents, they want a plan. We give them that plan.”
The plan, as it turns out, is about more than just artificial intelligence. It’s about survival. Factories today are operating in a world of volatility—tariffs, shifting transit fees and container costs that have tripled in nine months. The economics, as Hull put it, are “super backwards.” Factories commit to materials and labor before they even know if an order will be profitable.
“Over half of the factories taking these orders are saying they’re not even capable of paying the base fees for making this work,” he said. The result? “Projects that seem to promise a 7 percent margin can quietly bleed money, leaving manufacturers in the dark until it’s too late.”
Resonance’s CreateOne solution sits as an economic decision layer atop existing supply chain or ERP systems, running real-time calculations to answer a deceptively simple question: Are you making money? “We help them understand before they commit to the work,” Hull said.
Lawrence Lenihan, Resonance’s executive chairman and co-founder, leaned in to expand on the vision. The problem, he argued, isn’t just a lack of data—it’s a lack of truth. “We don’t say, ‘Hey, we sell AI.’ Everyone does,” Lenihan said. “What we’re solving for is this: Do you know your costs? And I don’t mean do you think you know your costs. Do you really know them?” The answer, more often than not, is no.
“Within a couple of sentences, you realize, ‘I don’t really understand my cost,’” Lenihan said. “And that’s the operating truth we’re after.”
The myth of the unchanging truth
Lenihan described the challenge as the difference between absolute truth and operating truth. “An operating truth is different from a truth you think of as inviolable, unchanging. The trouble with an operating truth is, it’s this truth—until suddenly, it changes.” A factory might expect 100-yard rolls of material, only to have 50-yard rolls arrive. “Now your truth is completely out the window. How do you react to that?” Traditional systems, often decades old, force workers to export data to spreadsheets—90 of them, in one case Lenihan cited. “That doesn’t work,” he said.
Resonance’s approach is rooted in lived experience. Lenihan and his team ran a plant, brands and a closed-loop system for nine years. They knew the pain firsthand. “We knew what came in, we knew what came out, but we didn’t know where we were making money and where we weren’t.”
The solution they built isn’t just AI—it’s AI plus swarms of agents, a memory system and a hybrid temporal graph network (HTGN), a neural net sitting atop a directed acyclic graph. “Everything is a deterministic output,” Lenihan said. “You put all these things together, and what we’re able to do is give you the real cost, right now.”
The profit puzzle
The real breakthrough, however, isn’t just knowing the cost; it’s knowing how to change it. “A factory is capability, capacity and a book of orders,” Lenihan said. “The problem isn’t that one order is profitable or not. It’s how do you run your whole business and make a profit?” Two unprofitable orders, for example, might become highly profitable when combined, thanks to shared operations that maximize efficiency.
“You may have two unprofitable orders, but if you could price them differently, or run them together, suddenly they’re very profitable,” he said. “It’s about organizing that portfolio of levers to maximize profit every day.”
This kind of calculation is complex, but once cracked, it unlocks everything from planning to scheduling. It even changes how factories bid on orders. “You can say, ‘I’ll charge you more per unit, but I’ll let you order in shorter runs, closer to when you need them,’” Lenihan said. “How do we generate more value together?”
The granularity is staggering. Resonance’s system accounts for everything—depreciation, maintenance costs, even thread expenses. “In these larger customers, shaving a nickel really matters,” Lenihan explained. For Western Hemisphere manufacturers, the key to competing isn’t just cheaper labor; it’s about delivering more value, faster, and closer to the market. “The real cost isn’t the labor—it’s the cost of not using the labor,” he said. “When that labor is sitting idle during changeovers, that’s the cost.”
From proof of concept to full deployment in three weeks
The speed of adoption is perhaps the most telling sign of CreateOne’s impact. “Imagine a [legacy tech company]’s implementation—congratulations, you’ve reached three years and you’ve got it in,” Lenihan said. “This thing went from proof of concept to full enterprise deployment in three weeks.” The reason? No cold start. The system trains instantly on what a company can do, delivering precise, actionable numbers. “It’s not giving generic advice,” Lenihan said. “It’s saying, ‘Here is the number. The number is 16,229.’”
Hull jumped back in with a real-time example. One customer, identity masked, had just pulled in nine orders. The system flagged a margin of 2.1 percent on the group, but 2.5 percent on the most profitable order. It also detected a sewing efficiency issue, possibly due to missing payroll on the floor.
“It’s giving them a read on what’s flowing through the system as a book of orders this week,” Hull said. The next step? “Do you want us to help you do something about it?”
Getting the band back together
Reflecting on his decision to reconnect with former colleagues Hull and Lenihan at Resonance, Rod Rozell, chief operating officer of Grupo Beta, led to a swift proof of concept and implementation of CreateOne. Upon reconnecting, he realized the platform had evolved into something transformative, ready to scale beyond its original scope.
Rozell stressed the importance of integrated AI across departments, warning that isolated use—such as different teams employing various AI tools without sharing insights—limits its potential.
He compares this to using a simple pivot table in Excel, arguing that true value comes from unification. Rozell said the capabilities of CreateOne and leveraging AI to transform operations, was akin to leaping far ahead of current capabilities. “We’re not going from steam locomotive to diesel electric; we’re going from steam locomotives to a SpaceX starship—something completely different.”
The speed of onboarding CreateOne also impressed Rozell, noting that interfacing with the platform took just weeks, a stark contrast to the months required by other systems. He praised its user-friendliness and interactivity, and highlighted how the platform’s rapid deployment and integration align with his ambition to redefine operational efficiency.
“We got wired on July 24th, and within weeks, we were ready to move from proof of concept to implementation,” he said.
The future: AI in the hands of the many?
Perhaps the most revolutionary aspect of CreateOne’s platform is who’s using it. “These aren’t engineers doing it,” Lenihan said. “These are people in planning, in finance, in operations. They’re putting this all the way down to the floor level.” The system even includes a prompt at the bottom: Ask your model. Early results show that the observations from frontline workers are as valuable as the data itself. “You’re seeing these experience-based observation points across the entire process,” Lenihan said. “More input, more input, more input.”
For Hull, the takeaway is clear. “Everybody wants to do this. Nobody has a strategy. We present that strategy.” And in an industry buffeted by volatility and uncertainty, that strategy might just be the difference between sinking and swimming.
As Lenihan put it, “The real single criterion is, you have to have rails that enable and guide it. You can’t have an answer that changes randomly. You’ve got to understand the industry.”
And after nine years of living the pain, Resonance does. Now, they’re giving factories the power to see the truth—and act on it.
