Tiger Global backs construction tech startup Assignar in $22 million raise

Cloud-based construction operations platform Assignar has raised US$16.5 million (A$22.3m) secondary financing round.

US VC giant Tiger Global led the round with local secondaries fund SecondQuarter Ventures joining in along with existing backers Fifth Wall, and Boston-based HighSage Ventures. Previous investors include Tola Capital and Our Innovation Fund.

Assignar has now raised more than US$30 million since it was founded in 2014 by Sean McCreanor and Marko Tomic. The latest raise gave early backers the chance to cash in some of their equity.

McCreanor, who relocated as CEO from Australia to the company’s other base in Boston, USA, two years ago, said he was thrilled to have such strong interest from tech and construction-related giants.

“This validation serves as further proof that our platform is a critical need for infrastructure and other construction sectors as it requires more project visibility and access to operations data to drive better results,” he said.

“Partnering with these firms will enable us to continue scaling and achieve positive outcomes for our growing customer base.”

Assignar offers an easy-to-use operations platform for self-perform general and subcontractors on infrastructure projects. It now operates in the US, Australia, New Zealand, and Canada and will look to Europe later in 2022.

The platform streamlines operations and project schedules as well as tracking crews, equipment and quantities, while improving quality and safety. Assignar measures and monitors productivity and progress with real-time data directly from contractors on the project.

McCreanor said the company has already doubled its headcount in 2021 and continues to invest heavily in R&D and product development to further develop its core platform.

The business is now looking to expand its offering in the first half of 2022 with project optimisation capabilities to createproject visibility from the owner through to specialty contractors, operations, schedulers and laborers, coupled with a recommendation engine that leverages data, AI, and machine learning to support planning and execution processes.