Industrial Next Says Its Robots Retool Factory Lines in Minutes

Industrial Next claims its AI-powered robotic stations can retool a production line in minutes instead of weeks — and BMW Group Technology Office has agreed to test that claim. The startup announced the BMW evaluation on stage today at TechCrunch Disrupt 2026 in San Francisco.

The company builds self-contained robotic stations running proprietary foundation models that link vision, task context and motion. It says manufacturers can reconfigure, combine and redeploy the stations across product lines, teaching new tasks through simulation and demonstration rather than hard-coded scripts. That is a bold promise; line changes at most factories still take weeks or months of reprogramming and physical retooling. In plain words: Industrial Next claims the most expensive bottleneck in manufacturing can be compressed by orders of magnitude — but the claim rests on demo-stage performance, not audited production data.

Who is behind the company?

Founders Lukas Pankau and Allen Pan met at Tesla. Pankau was a lead electrical architect there before joining Waymo; Pan led Tesla's autonomous factory efforts and later became a chief architect at Neolix, an autonomous driving company. They founded Industrial Next in 2021.

Their motivation, they say, was frustration with aging factory equipment holding back advanced vehicle and self-driving technology. Pankau put it bluntly: "You had them effectively stumped at the factory looking at this equipment from the 1980s. And so we started rethinking what this should look like using the technology that we have today and using everything that we know today."

The two kept comparing notes from separate jobs before committing. Pan recalled the trigger: "We're like, we're not getting any younger, and we're still obviously passionate about it. We still keep complaining about industrial technology. Like, this is three years later, we should start the company." Takeaway: the company grew out of a specific, lived pain point at Tesla, not a market analysis.

What does the technology actually solve?

Traditional industrial robots execute narrow, repeatable tasks. A small change in product or process can make them fail or trigger costly downtime — which is why manufacturers often simplify products so machines can handle them. Flexibility is the harder problem: robots need enormous training data, and robotics lacks the internet-scale datasets behind language models.

Pankau conceded the gap. "We can make robots do all these cool things like dance around, because there's data for that, but we can't make them assemble high-precision things," he said. "This is where we've made a lot of progress on using simulated environments to do these high-precision and high-dexterity tasks. The secret sauce for the robots that we have is that we have given them an understanding of the process in general, but we build that understanding using an extremely high-fidelity simulation engine that is close to the real dynamics to where we can let the robot do these kinds of high precision tasks almost without seeing the parts at all."

In practice, the robots train in a virtual world that mimics real physics before touching a factory floor. Takeaway: the differentiation is simulation fidelity, not raw data volume — and that fidelity is the part independent observers cannot yet verify.

Where are the robots already working?

Pankau said some workstations already operate in production of one of the world's most popular smartphones, though he would not name the customer. He cited tasks that manufacturers had tried and failed to automate for years: threading a delicate flex cable and snapping a tiny connector cap into place.

He also described a "film peeling" station where the robots strip protective film off camera modules and buttons at or above human speed with 100% accuracy — even after being moved to a new phone model and production line they had never seen. A 100% accuracy claim, it should be noted, came from the founder himself, not from the undisclosed customer.

What comes next?

"We started this company because that is the kind of performance that we wish we had at Tesla, where we were bypassing robot stations because we couldn't program them effectively enough using traditional technologies," Pankau said. "Now we want to take this to more complicated tasks than just building a phone. We ultimately want to take this to building a full car end-to-end without any human touching ever."

The BMW evaluation will be the first public test of whether phone-assembly performance transfers to automotive-scale complexity.