Nexar Lays off 17 Employees in Third Round of Layoffs

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Nexar, the AI computer vision company known for its crowd-sourced visual data, has undergone its third round of layoffs, resulting in the departure of 17 employees, equivalent to 14% of its workforce. This series of layoffs follows earlier staff reductions, with 25 employees leaving in November 2021 and another 12 in July 2023, amounting to an overall workforce reduction of more than 30% within the past 10 months.

In November 2021, Nexar secured $53 million in a Series D funding round, led by Qumra Capital, with participation from State Farm Ventures, Catalyst Investments, Banca Generali, Valor, and existing investors like Atreides Management and Corner Ventures. This funding was pivotal for Nexar’s expansion and technological advancement.

Nexar, established in 2015 by CEO Eran Shir and CTO Bruno Fernandez-Ruiz, specializes in gathering crowd-sourced visual data. The company collects data from numerous dash cameras installed in vehicles across the United States and globally. This collected footage is then subjected to analysis using artificial intelligence (AI), providing valuable insights to various stakeholders, including municipalities, transportation departments, automotive manufacturers, and insurance firms.

One of Nexar’s groundbreaking applications involves creating AI-driven digital replicas of U.S. roadways and their surroundings by leveraging the collected visual data. This tool empowers city officials to expedite urban planning, prioritize projects, and address issues more efficiently. Additionally, automotive companies utilize this data to enhance the situational awareness of their autonomous vehicles, helping them navigate obstacles and challenges on the road.

Despite the recent workforce reductions, Nexar remains a prominent player in the AI computer vision sector. With its cutting-edge technology and extensive visual data resources, Nexar continues to offer valuable solutions that enhance urban planning, transportation systems, and the development of autonomous vehicles.

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