Teleo wants to help the robotics industry reach its 'ChatGPT moment ...
Teleo describes itself as a construction robotics startup, but its mission is bigger than automating heavy equipment like excavators and tractors. Today, Teleo’s retrofitted machinery allows its customers to operate their existing fleets semi-autonomously. In the future, the startup sees the data it collects as a key enabler for the robotics industry to reach its “ChatGPT moment.”
That isn’t an aspiration to reach the same level of hype surrounding ChatGPT. Instead, Teleo CEO Vinay Shet sees an opportunity for robotics companies — and namely the one he runs — to gather vast datasets similar to amounts used to build ChatGPT in order to make big, game-changing leaps in robotics.
Funding and Growth
And investors seem keen to help the startup reach that milestone. TechCrunch has learned that Teleo recently raised $16.2 million in funding through two extensions to its 2022 Series A round. The $9.2 million extension closed in April and another $7 million one closed this week, according to recent filings and information from the company.
Teleo aims to bridge the gap in robotics data by logging data from its own daily operations, which Shet says will end up “becoming the basis upon which you can train true robotic foundation models” that can lead to generalized intelligence.
Operational Strategy
To build a diverse dataset, Teleo has expanded beyond construction and is deploying autonomous heavy machinery across a range of industries. The hope is that the data collected will allow Teleo to fine-tune or specialize foundational robotics models, potentially enabling the replacement or augmentation of humans with cloud-based AI agents capable of controlling different machines.
Teleo’s recent funding rounds were led by UP.Partners with participation from new investors Trousdale Ventures and Triatomic Capital, as well as returning investors F-Prime Capital and Trucks VC, among others.
Future Goals
Teleo plans to use the funds to scale customer deployments, expand to new industries, and enhance its AI capabilities, including integrating large language models to improve operator efficiency.
“Over the next several years, you will see vertically integrated companies like ourselves actually deploy in the real world in a manner that makes sense economically speaking and grows economically based off that,” Shet said. “But along the way, they’ll collect enough data in the right format so that it unlocks that ‘aha’ moment a few years down the road.”
