Oxford University-backed image platform Zegami snaps up £2.3m funding
The Series A funding will be used to “move the software to the cloud to make it more accessible to everyone”
Image management platform Zegami is celebrating having raised £2.3m in a Series A funding round led by Oxford Sciences Innovation, with participation from Parkwalk Advisors.
An Oxford University spinout company founded in February 2017, Zegami’s image search and data exploration platform enables the easy navigation and analysis of large collections of image-rich data – from personal photo albums to scientific imagery.
The platform counts museums and galleries among its global roster of clients, as well as universities such as Oxford University, the University of South Australia, Edinburgh University and Oregon University.
The software has also been taken up by organisations in the human resources, asset management and sports analytics sectors, including HR company SD Worx and product development form Innova Systems.
Zegami plans to use the funding to enhance marketing efforts and bring more employees on board as it works to translate its platform into an accessible product for the consumer market.
This news comes as Zegami has announced the donation of its platform to the Oxford University-led Lest We Forget campaign, which aims to digitise memories and memorabilia from World War I in a public archive.
Samuel Conway, chief executive officer at Zegami, said:
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“The investment marks a significant step forward for the company enabling us to grow our team and marketing efforts as we move the software to the cloud to make it more accessible to everyone.”
Stephen Taylor, co-founder at Zegami and head of computational biology at the Weatherall Institute for Molecular Science at Oxford University, said:
“Using Zegami we’re able to make sense of the vast collections of image data and its associated metadata, which is key to our research in ways which were previously impossible.
“The ability to use multiple parameters to search, sort and group images is invaluable for picking previously unseen patterns or characteristics in the image datasets.”