The industrial data analytics provider has been backed by the Stanford-StartX fund, after graduating from the accelerator program in 2016.

Arundo Analytics, a US-based industrial data analysis graduate from StartX, the accelerator affiliated with Stanford University, raised $25m in series A funding yesterday from investors including the Stanford-StartX Fund.

The round featured venture capital firms Northgate Capital and Horizon Ventures, as well as investment firms Sundt, Canica and Strømstangen, family office Stokke Industri and Arctic Fund Management.

Arundo was founded in 2015 and graduated from Stanford-StartX one year later. It provides cloud-based data analytics that help heavy industry operators aggregate data from their facilities, which can then be formulated by customisable machine-learning models that plug in to Arundo’s platform.

The suite includes the ability to collect data “on-edge”, from processing units positioned close to industrial facilities that suffer from poor connectivity, caused by factors such as remote locations or inhospitable climates.

The cash will be used to expand Arundo’s sales and marketing efforts in heavy industries such as oil and gas, maritime, mining, chemicals, power and manufacturing.

The money will also drive recruitment of software engineers and data scientists across Arundo’s offices in Houston, Oslo and Palo Alto.

Arundo has now raised $32.5m in funding altogether, including $4.9m in a 2016 round, according to deals database PitchBook, which featured StartX, Northgate Capital, Alliance Venture and TRK Group.

Technology consultancy Brillio made a strategic investment in 2015, reportedly as part of a $2.7m seed round that also included Alliance and Northgate.

Jakob Ramsøy, co-founder and chief executive of Arundo, who was an entrepreneur-in-residence at StartX until October 2017, said: “This investment is a validation of the product and market strategy our team pursued over the last two years.

“We created flexible, user-friendly software that allows operators, original equipment manufacturers and service companies in heavy industries to quickly integrate machine learning into their operations.”