V7 Raises $33 Million In Series A Funding Round The fund raised will allow V7's further expansion into the US market, growing its team in its biggest market
By Teena Jose
Opinions expressed by Entrepreneur contributors are their own.
You're reading Entrepreneur Asia Pacific, an international franchise of Entrepreneur Media.
V7, the data engine to build and improve AI for computer vision, has raised $33 million in Series A funding round co-led by AI-focused Radical Ventures and Temasek, along with participation from existing investors Air Street Capital, Amadeus Capital Partners, and Partech. The fund raised will allow V7's further expansion into the US market, growing its team in its biggest market.
"The next generation of software won't run on code, it will run on models fuelled by training data. We let our clients easily turn human knowledge into AI models, improving the safety and accuracy of AI. This is incredibly powerful, and in the right hands, can literally save lives. If we want to build software that tackles unsolved frontiers in human health, or our climate, this is the only way", said Alberto Rizzoli, CEO of V7.
V7's growth trajectory is unabated despite a macroeconomic downturn. In 2022, the business grew ARR by 3 times, organic traffic to the V7 website increased by 100 times and the team grew headcount by 5 times to meet significant market demand for the product. With more than 300 clients including GE Healthcare, Paige AI, and Siemens, V7's customers are deploying AI technology to support faster release of AI models in mission-critical applications, claimed by the company in a statement.
"As data proliferation continues, intelligent data orchestration is essential for businesses looking to deploy machine learning models. V7 is well-positioned to become the industry-standard for managing data in modern AI workflows," said Parasvil Patel, partner, Radical Ventures.
Led by Alberto Rizzoli and Simon Edwardsson, V7 automates the labeling process, allowing companies to solve data labeling tasks ten times more quickly. The company's unique programmatic labeling workflows use AI models and minimal human steering to apply labels to data at scale.