Industrial Next wants factory robots to switch tasks in minutes, not weeks

In the modern manufacturing landscape, automation is often synonymous with rigidity. Industrial robots are typically tethered to a single, repetitive function, designed to perform one specific task for one specific product. When a manufacturer needs to pivot—whether to update a design or introduce a new product line—the process is notoriously grueling. It often requires weeks or even months of downtime, involving extensive reprogramming, physical retooling, and the complete replacement of specialized equipment.

San Francisco-based startup Industrial Next is looking to dismantle this status quo. Making its debut on the Startup Battlefield stage at TechCrunch Disrupt 2026, the company unveiled its vision for AI-driven, self-contained robotic stations. By leveraging proprietary foundation models that synthesize vision, task context, and motion, the startup claims it can retool a production line in mere minutes, bypassing the need for traditional, labor-intensive programming.

Bridging the Gap Between Design and Execution

The mission behind Industrial Next is to enable manufacturers to automate delicate, complex tasks that have historically remained the exclusive domain of human labor. To validate this technology, the company announced a strategic partnership with the BMW Group Technology Office, which will evaluate the startup’s AI-powered dexterous manipulation capabilities for automotive manufacturing applications.

The company was founded in 2021 by Lukas Pankau and Allen Pan, two veterans of the Tesla production ecosystem. Pankau previously served as a lead electrical architect at Tesla before transitioning to Waymo, while Pan led Tesla’s autonomous factory initiatives and later served as a chief architect at the autonomous driving firm Neolix.

The origin story of the company is as unconventional as its technology. The pair first crossed paths at Tesla, where their initial encounter involved Pankau literally tripping over Pan, who had fallen asleep on a factory catwalk after an exhausting all-nighter. Despite a rocky start, they discovered a shared obsession with operational efficiency and complementary technical skill sets. After years of witnessing cutting-edge automotive and self-driving software advancements being stifled by outdated, 1980s-era factory hardware, the duo decided it was time to rebuild the foundation of manufacturing.

"We had all these fancy things that we could do on cars. I could run machine learning algorithms to design a car for me. But I couldn’t figure out how to program a robot, and this was pretty common for engineers at Tesla," Pankau noted. "You had this massive divide... you had people who are really good at what they’re doing—designing cars and designing self-driving systems—and then you had them effectively stumped at the factory looking at this equipment from the 1980s."

Solving the Data Problem in Robotics

The founders spent years ruminating on their frustrations with industrial equipment even after moving on to new roles at Waymo and Momenta. Eventually, the desire to solve these systemic issues became impossible to ignore. They realized that while the world had made massive strides in software, the physical world of the factory floor had stagnated.

The primary hurdle in making robots truly flexible is the scarcity of training data. Unlike large language models that can scrape the internet for information, robotics lacks the massive, diverse datasets required to teach machines how to perform nuanced physical actions.

• The Simulation Advantage: Industrial Next utilizes high-fidelity simulation engines that mirror real-world physics.
• Process Understanding: Rather than relying on rigid, hard-coded scripts, the robots are taught through simulation and demonstration.
• High-Precision Dexterity: The system allows robots to execute complex tasks with minimal visual input by relying on a deep, generalized understanding of the manufacturing process.

"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," Pankau explained. "The secret sauce for the robots that we have is that we have given them an understanding of the process in general... we can let the robot do these kinds of high-precision tasks almost without seeing the parts at all."

Real-World Impact and Future Ambitions

The efficacy of Industrial Next’s technology is already being tested in the wild. The startup’s workstations are currently deployed in the production of one of the world’s most popular smartphones. These robots are successfully executing tasks that manufacturers have struggled to automate for years, such as threading delicate flex cables and snapping tiny, precision-engineered connector caps into place.

Another standout application is a "film peeling" station. In this scenario, the AI-powered robots strip protective films off camera modules and buttons at speeds equal to or faster than human workers, maintaining 100% accuracy even when the system is moved to a completely new phone model or production line it has never encountered before.

For the founders, this is only the beginning. Their ultimate objective is to move beyond consumer electronics and tackle the automotive industry head-on.

"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," said Pankau. "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."

By replacing rigid, legacy infrastructure with intelligent, adaptable robotic cells, Industrial Next is positioning itself as a critical player in the future of smart manufacturing. As they continue to scale their partnerships and refine their simulation-to-reality pipeline, the dream of a fully autonomous, flexible factory floor is moving closer to reality.