New Stone completed a nearly $400 million Series E financing, with the National Artificial Intelligence Industry Investment Fund leading the investment
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Autonomous driving technology company Neolithic announces completion of nearly $400 million in Series E financing. Officials state that this round was co-led by Boyu Investment and a national-level fund, while sources familiar with the matter indicate that the National Artificial Intelligence Industry Investment Fund also participated in the lead investment; the company stated that it will use the funds for building a global fulfillment network and investing in end-to-end large model data training closed-loop systems.
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News from the IT Home on October 20th: Autonomous driving technology company Neolithic (Neolix) today announced the official completion of a Series E financing of nearly 400 million US dollars (IT Home's note: The current exchange rate is approximately 2.677 billion RMB).

Official announcement: This round of financing is jointly led by Boyu Investment and a national-level fund, with Gaohu Capital serving as the exclusive financial advisor for this round of financing.

According to Jiemian News, sources in the investment community revealed that Boyu Investment and the National Artificial Intelligence Industry Investment Fund are the leading investors in Neolithic's current round of financing.

IT has learned that this round of financing has also set a new record for the largest single financing deal in the domestic autonomous driving sector this year. To date, Neolithic Company has raised a total of over ten billion yuan in funding, ranking first in the industry for unmanned delivery vehicles.

According to the official statement, after this financing is completed, Neolithic will focus its funds on two main areas:

  • First, we will deepen the refined operation of our network, accelerate the localization of service centers in major global regions, and build a more responsive and efficient global customer fulfillment network.
  • Secondly, increase investment in the end-to-end large model data training loop to further enhance the safety level of autonomous driving and vehicle performance, while continuously reducing the operational and maintenance costs throughout the vehicle's lifecycle.
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