One of the most important things happening on Earth today is the accumulation of solar energy. Around the world, companies and countries are racing to deploy solar and batteries to achieve energy independence and limit the effects of climate change.
However, these expansions face labor market challenges, with a limited supply of workers to meet the growing demand for installations. Robots may 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 these equations.
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 came out of stealth Tuesday morning with a $26 million Series A funding round led by Obvious Ventures with participation from Union Square Ventures and Active Impact Investment. Following an initial seed round backed by First Round Capital, Climactic, Congruent Ventures, and VSC Ventures, the total funding amounts to $34 million. The startup is building intelligent systems that “help civilization build infrastructure faster,” according to Puri.
Puri told TechCrunch: “Our argument is that if you really want to speed up construction, you need intelligence that can operate in the outdoor chaotic environment of these construction sites, and it needs to be generalizable enough to operate in these different environments.”
Rather than building its own robots from scratch, Gritt uses off-the-shelf hardware (so far rented skidders and robotic arms made by companies like Kawasaki) to build a platform controlled by an AI model. The first task the system handles is to unload the large glass solar panels, transport them towards the metal frame where they need to be installed, and place them on the frame with sub-millimeter accuracy so that workers can secure them.
“You had people who were building rockets to space and had infinite budgets for the smallest parts, and you had people who knew what it meant to go in and do the dirty, boring, dangerous work and scale it like crazy,” said Andrew Beebe, a partner at Obvious Ventures, who led Gritt’s Series A round. “These people are in the second camp. They are a special breed of entrepreneurs with technical skills, AI and machine vision skills.”
Gritt is currently deploying two systems in the field and using the data they collect to improve behavior. Puri says a typical eight workers can install 800 panels per day, but the same workers using Gritt’s system can install 3,000 to 4,000 panels per day.
Now the company says it has a contract 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 operational within the next six months.
TechCrunch spoke with one Gritt customer who declined to be identified for competitive reasons but was enthusiastic about the system’s ability to improve operations. He expects it will make it easier to work in remote sites where it’s difficult to attract workers, and that injuries will be reduced because workers won’t have to repeatedly lift 100-pound panels above their heads.
Gritt competes with companies that have their own panel installation robots, such as 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 the Gritt. This is a difference that can determine who can grow faster and have a lower cost structure as demand increases.
Gritt wants to add new manipulation tasks to the system, allowing it to secure solar panels, install drill posts and even build racks to sit on top of them. In the long term, they want to transition to other common, labor-intensive construction tasks, such as tying rebar before pouring concrete.
What drives the startup to pursue this vision? The founders’ main argument is the emergence of a new AI model.
“To what extent was it possible to create a system for one solution even five years ago?” Puri said AI is now generalizing that task, allowing the same basic pipeline to be reused and improved across tasks. For example, he noted that while training a system for laying concrete blocks took several weeks, a similar demonstration using rebar binding was completed in just one day using the same software.
But training for new tasks is only the beginning of Gritt’s vision. The founders believe the array of sensors and intelligence the system brings to the workplace can do more than install panels. This can improve management and decision-making. For example, we imagine a system could detect that ditches are open as a storm approaches, alerting workers to cover them before the rain damages parts, or flagging missing inventory.
“Grit is now a physical AI layer that performs these skilled, labor-intensive tasks and can help make decisions on site,” Puri said.
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