“I realized that there was a huge gap between what I was seeing at Accel and in the Valley, with us investing in companies like Anthropic and Lovable in Europe,” he said, “and what I was seeing in the more industrial parts of the economy.”

He said that most AI has targeted so-called knowledge workers who complete their jobs using software, but that many more jobs in the economy involve the production and movement of physical goods. “Most AI investment to date has targeted the 30% of workers behind a desk. The real ROI lies in the 70% running industrial operations,” he said. 

Sonali de Rycker, the Accel partner who backed Arrakis’s seed round, said she is betting on the founder as much as the market. “Rafa has a rare combination of curiosity, hustle and tireless drive,” she told Fortune. “After working closely with Rafa during his time at Accel, it’s an honour to be working with him again as an entrepreneur.”

But Arrakis is hardly alone in going after manufacturing and industrial firms. Consulting giants such as Accenture and Boston Consulting Group are racing into industrial AI, as is Palantir, and Jeff Bezos-backed Prometheus—now valued in the tens of billions of dollars—is pouring capital into automating the engineering of physical products. The frontier labs are circling too.

Quintanilla argues Arrakis is carving out a distinct niche from each of these competitors. If Prometheus worked with Airbus, he said, it would build AI for “the core engineering of building an aircraft.” He said Arrakis, by contrast, “want[s] to take care of everything around it… We want to be the AI layer for key operations of those companies.”

As for Palantir, Quintanilla said he respects the company but that its tech was not built to be AI-native. “Palantir is a fantastic company. I think half of my team currently comes from Palantir,” he said, noting that, among others, those hires include a former head of Palantir’s procurement and supply-chain team. “However, Palantir is a 20-year-old company that has a very hefty price point, that has a technology that is starting to become legacy.”

And Quintanilla says consulting firms, even as AI has begun to change their business model, still often have an incentive to charge for either consultant hours or outsourced human labor. “If you think about what consulting firms are, they solve strategy problems for companies with human as the key enabler,” he said. “We want to solve problems for companies with software and human as key enablers.”

He said he is skeptical of the sweeping “process transformation” that consultants often sell. “The big project transformation pitch sounds very sexy on paper, and can be great if you want to pump your stock in the short term,” he said, “but what I’m hearing in the boardrooms where I’m sitting is that there is a lot of fatigue from CEOs on having vendors that do not want to commit to short timeline[s].”

Instead, Arrakis starts small, he said. For one New York-listed shipping company—which he said he could not name due to non-disclosure agreements—the goal was to improve cash-flow visibility from monthly to daily. Arrakis’s engineers rebuilt the spreadsheet operators already used, having AI populate the data while the system “learns and starts to codify the knowledge of those operators” as they make corrections. The playbook, he said, “always starts with HQ, prove the value, move to field operations as soon as you get the pull to get there.” Arrakis usually charges about half of its fees for hitting a particular performance target, he said.

Landing conservative European industrial firms is its own challenge. Quintanilla offered what he called “an open secret”: his best traction has come from family-controlled businesses. “They think long term, they can push for top-down initiatives to be executed, and I can build non-transactional relationship[s] with those people.”

Arrakis is model-agnostic by design—a stance Quintanilla said resonates with executives worried about being locked in to a single AI model provider and wary of high token costs. He said one Swiss C-suite executive told him: “When we started this, everyone told us we had to be on Copilot. Then we went to OpenAI. Now it’s Anthropic. My head is going like this… I basically want someone who is able to route me to the best provider.”

He said the company typically starts building using proprietary models from OpenAI or Anthropic, and then shifts customers to open-source alternatives—such as those from Mistral, or, if the customer permits, Chinese vendors—wrapped in a “fat harness” that he claims delivers a two-to-four-fold quality improvement while cutting token costs by roughly 70%.

Arrakis currently has five customers and plans to triple its headcount from roughly 15, opening outposts in New York and the Middle East.