Israeli artificial intelligence (AI) and machine learning company Aquant announced that it has raised $2.6 million in seed financing round from World Trade Ventures, SilverTech Ventures, AngeList Syndicate led by Gil Dibner, and a group of private investors. The funds will be used to expand the company's customer base and speed up development of its technology's capabilities.
With its development offices in Tel Aviv and headquarters in New York, Aquant has developed AI and machine learning technology to address the multi-billion-dollar problem of machinery downtime that troubles service companies. The unique aspect of Aquant's technology is its ability to locate potential failures at levels that are difficult to be predicted.
Aquant says that companies in the service industry are used to paying heavily for machinery downtime and recurring technicians' visits, due to lack of technicians' skills and wrong stocking of parts. Aquant provides service companies with a permanent solution for this problem.
Aquant was founded by CEO Shahar Chen and COO Assaf Melochna. Chen said, "The downtime problem is a $647 billion a year problem. It still amazes me that service companies have let this problem go on for so long. It takes an amazing, experienced, dedicated team with the specific knowledge of the industry and of AI to change things and solve this problem. We are really proud of the team at Aquant.io for providing the solution the industry has been looking for."
Engineered with predictive AI and machine learning, Aquant's algorithms are able to forecast the servicing needs long before they occur and save precious downtime, all based on the analysis of existing historical structured and unstructured data.
Melochna said, "As one of the pioneers to introduce artificial intelligence and machine learning to the space of service, Aquant is creating a revolution by eliminating the need to replace existing IT systems at an excessive cost. Instead, Aquant turns those IT systems into intelligent ones by providing a 'brain' that is able to collect and analyze any format of data in a flash and generate predictions for expected technical failures of the machine, the most cost-effective solution and the required parts and skills in order to increase uptime."
Published by Globes [online], Israel business news - www.globes-online.com - on November 8, 2017
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