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Artificial intelligence (AI) software solutions provider FireVisor has raised seed funding of “close to $1 million” in an exercise led by the 500 Startups Durians II Fund and Acequia Capital and joined by SGI along with Entrepreneur First.

FireViso is a Singapore start-up which has built a machine learning enabled analytics platform that connects to data sources in the manufacturing line, and automatically performs engineering failure analysis in real-time. Its media release on Tuesday (March 19) did not specify the quantum of the funds raised, except that it was “close to $1 million”.

FireVisor pioneered the concept of cognitive automation in manufacturing, according to the statement. The founders, Surbhi Krishna Singh and Long Hoang, believe that the industry has been focusing for too long on simple “if-else” based automation which provides little improvement potential for today’s factories. As an alternative, the start-up provides cognitive solutions that are powered with artificial intelligence.

Launched in 2018, the company has a client base in South-east Asia, India and China, including manufacturers such as REC Solar. It has more than 1.3 million parts in its industrial data set and boasts of accuracy above 98.5 per cent when making decisions with manufacturing data. While this saves 45 per cent engineering time, it also “helps manufacturing companies save costs otherwise lost on product failures and quality issues”.

FireVisor chief executive and co-founder Ms Krishna Singh said: “Today, an army of human inspectors are needed to make sure no fault escapes the production line, while process control engineers constantly tweak and monitor machines. The machines, on the other hand, produce an abundance of valuable data, but these vast amounts of data are unused and simply get discarded. FireVisor is on a mission to bring the power of data science to the manufacturing floor, and in the hands of humans with a few clicks.”

Said FireVisor’s chief technology officer and co-founder Mr Long: “Our secret weapon is our capability in dealing with data. Manufacturing data spread out in different systems have gaps, and contain false information. Our machine learning models work so well because we are able to clean the data, fill in the missing information and then bring them all together in one platform.”

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