A need for clean: this startup is automating data cleaning to keep up with AI demand
Artemis Data has raised $2 million in pre-seed funding to make data cleaning 50 times faster
To build artificial intelligence you need droves of data. But if your data isn’t clean, the intelligence it produces won’t be either. AI systems learn from the data they are trained on, so any errors or inconsistencies in the data can lead to incorrect or unreliable outcomes. According to IBM, data scientists spend around 80 per cent of their time cleaning and organizing data, highlighting the importance of clean data in AI development.
Velocity-linked startup Artemis Data has built software to make the data cleaning process fast and efficient and has raised $2 million in pre-seed funding to expand its team and onboard users. Pre-seed funding is the initial capital raised by a startup, to fund operations and product development. Raven Indigenous Capital Partners, led this funding round with participation from Telegraph Hill Capital, Ripple Ventures and other angel investors.
“The winners in the world of artificial intelligence are organizations that have clean and accessible data,” says co-founder Josh Gray. “Today, the tools to clean and transform data do not allow teams to scale their work fast enough to keep up with the demand for insights and new AI products. Artemis is here to change that.”
Co-founder William Shi (BASc ’23) says the software platform, which is currently in beta testing with about 30 customers, is all about accelerating team speed for data reporting and cleanup. Gray and Shi have been friends since middle school. They reconnected ahead of the popular Collision Conference in Toronto two years ago where Gray was presenting the idea behind Artemis. Shi was studying mechatronics engineering at the University of Waterloo at the time and wanted to build the product.
