News that German proptech startup Building Radar has raised €5 million in Series A funding makes great sense when you take into account Germany's booming construction industry.
Founded by Leopold Neuerburg, Paul Indinger and Raoul Friedrich in 2015, Building Radar aims to digitize the construction industry leveraging AI and other emerging technology. According to the startup, the round, led by PropTech1 Ventures and Coparion, will be used to tap into new markets and refine its search algorithm and optimize results via machine learning.
Building Radar uses AI to identify new construction projects worldwide. By identifying and presenting these projects early on in the building process, material suppliers and service providers can capitalize on opportunities more efficiently. PropTech1 Ventures Managing Partner Anja Rath told Tech.eu:
“When it comes to digital innovations, the construction industry has enormous growth potential. Accordingly, we are pleased to invest in Building Radar, a PropTech that, with the help of artificial intelligence, frees suppliers in the construction industry from extremely idle manual processes at the sales level.”
The software used by Building Radar identifies more than 5,000 new construction projects per day from 100,000 plus sources. Platform users search by location, size, phase, and other criteria, and end up getting push notifications on the most current projects.
The startup is interesting for me because it's basically an advanced search engine that indexes millions of articles about construction-related projects. For some years now I've wondered if more innovative companies would begin creating their own unique engines for just such purposes. Google, having monopolized general search, is actually not very effective as it once was. Using advanced market intelligence powered by tailored search algorithms will obviously lead to boosted conversions and revenue.
New investors in the startup include SIGNA Innovations, an Austrian SIGNA Group subsidiary and Ferdinand Oetkar via FO Holding.