One of the most important things happening on Earth today is the enhancement of solar energy. Companies and countries around the world are racing to deploy solar power and batteries to achieve energy independence and limit the effects of climate change.
But its construction faces labor market challenges, with a limited supply of workers to meet the growing demand for installations. Robots could be the answer, but industrial robots have historically struggled in unstructured environments, at least until now. The latest generation of AI models may have changed that equation.
That’s the driving idea behind Gritt, a startup founded by two Carnegie Mellon University-trained roboticists, CEO Puneet Puri and CTO Vishal Dugar. The company quietly closed a $26 million Series A round of funding on Tuesday morning led by Obvious Ventures with participation from Union Square Ventures and Active Impact Investment. This brings total funding to $34 million following an earlier seed round backed by First Round Capital, Climactic, Congruent Ventures, and VSC Ventures. In Puri’s words, the startup is building intelligent systems to “help civilization build infrastructure quickly.”
“Our thesis is that if we really want to speed up construction, we need intelligence that can work in the chaotic environment outside of a construction site, and it has to be versatile enough to work in these different environments,” Puri told TechCrunch.
Rather than building its own robots from scratch, Gritt uses off-the-shelf hardware (so far, rental skidders and robotic arms made by companies like Kawasaki) to build a platform controlled by its AI models. The first job the system handles is to unload large glass solar panels, carry them toward the metal frame where they need to be installed, and position them with sub-millimeter precision on the frame so that workers can secure the panels.
“Some people used to build rockets to go to space and had endless budgets for tiny parts, and some people took dirty, boring, dangerous jobs and knew what it meant to scale like crazy,” said Andrew Beebe, a partner at Obvious Ventures, which led Grit’s Series A round. “They are in the second camp, a special breed of entrepreneurs who have the technical talent, AI and machine vision skills to make it work.”
Gritt currently has two systems in the field and uses the data it collects to improve operations. A typical eight-person crew can install 800 panels in a day, Puri said, but the same crew using Grit’s system can install 3,000 to 4,000 panels in a day.
The company says it currently has contracts in place to help install 2.8 gigawatts of solar panels over the next 18 months, and its customers include three of the top 10 U.S. power construction companies. The company hopes to have 48 systems up and running within the next six months.
TechCrunch spoke to one Grit customer, who declined to be named for competitive reasons, but was enthusiastic about the system’s ability to improve their work. He expects it will make it easier to work in remote locations where it’s difficult to recruit workers, and he also hopes to reduce injuries because workers won’t have to repeatedly lift 100-pound panels over their heads.
Grit competes with companies that have their own panel-installing robots, including Luminous Robotics, Cosmic and China’s Trinabot. These companies are building their own hardware rather than focusing on off-the-shelf vehicles and weapons like Grit, a difference that could shape which grows faster and with a leaner cost structure as demand increases.
Gritt wants to add new operational tasks to his system, allowing him to secure solar panels, drill posts, and even build the racks on which they sit. In the long term, the company hopes to tackle other common labor-intensive construction tasks, such as tying rebar before pouring concrete.
What has enabled the startup to pursue this vision? Primarily, the founders say, is the rise of new AI models.
“It was still somewhat possible to systemize a solution five years ago, right?” Puri said, but AI has made that work generalizable, allowing the same underlying pipeline to be reused and improved across tasks. As an example, he noted that training a system for stacking concrete blocks took several weeks, but a similar demonstration involving rebar tying took just one day using the same software.
But training for new tasks is just the beginning of Grit’s vision. The founders believe that the suite of sensors and intelligence their system brings to the field can do more than just install panels. It can facilitate management and decision making. For example, they envision the system being able to notice when a storm is approaching when a ditch is open and alert workers to cover it before rain damages parts or flag low inventory.
“Gritt becomes this layer of physical AI that can not only perform this dexterous, labor-intensive task, but also assist with decision-making in the field,” Puri said.
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