Maxinsights, a company developing data technology for physical AI, has raised $30 million in seed funding. The round was led by Peak XV, with participation from Tola Capital, South Park Commons, INCE Capital and Alumni Ventures.
Alongside the funding announcement the company launched two new products: MaxLoop, a system that checks and corrects data as it is collected, and MaxGlove, a sensor-equipped glove designed to capture touch and force data. These tools aim to improve how data is collected and used to train physical AI systems.
“We built the whole pipeline ourselves, from the hand-tracking models to the platform that scores every operator’s footage as it arrives, because nobody was going to sell us the pieces that make messy real-world data usable,” said Yuguang (YG) Yuan, founder and CEO of Maxinsights. “Robots get deployed into real kitchens, not staged ones, and something unexpected happens every few minutes. Edge cases are the job, and the only way to get them is to record real work in real homes. Collecting one great hour of data is easy, and holding quality across millions of hours and thousands of people is what we built the company to do.”
“Almost every company in this category is selling hours. Maxinsights is the only one that treated quality control as an engineering problem rather than a staffing cost, which is what lets them operate in real life, said Rohit Agarwal, Managing Director of Peak XV (ex-Sequoia India). “That decision is hard to copy after the fact, and it’s why the labs that tried other vendors keep coming back to them.”
“Robots need to learn from the complexity and variation of everyday life, and carefully controlled datasets alone cannot capture that,” said Johnson Wang, cofounder of Genesis AI. “With Genesis 26.5, we focused on footage of people performing everyday tasks naturally, with much of that data provided by Maxinsights, to help our models learn from a broader range of real-world situations”
Maxinsights develops technology that helps collect and prepare real-world data to train physical AI systems and robots. It captures how people perform everyday tasks and turns that information into training data that robots can use.
Instead of creating artificial test environments, the company records people doing their regular work in homes, restaurants and hotels across eight countries. It then uses its own AI models to check the data quality, add labels and organise the information so it is ready for robot training.
