ZaiNar Raises $100 Million for AI-Powered GPS Alternative

California-based startup ZaiNar has raised $100 million to develop an artificial intelligence (AI)-powered GPS alternative.

That $100 million figure includes the $10 million the company recently took in, valuing it at $1 billion, the company said in a Thursday (Feb. 19) press release.

ZaiNar said it addresses an ongoing problem for robots and other AI-powered systems: the ability to get exact location data that helps the bots detect other people and objects.

The company said it has come up with an alternative to GPS that uses Wi-Fi and 5G cellular networks to deliver superfast location information without satellites, cameras or battery drain.

“ZaiNar has solved a problem that’s stymied the industry for decades,” said Steve Jurveston, who sits on the company’s board, as well as that of SpaceX. “Precise positioning without dedicated hardware infrastructure.”

CEO Daniel Jacker told The Information in an interview published Thursday that the company has attracted business customers in sectors including hospitals and construction.

In the former setting, ZaiNar’s tech can alert staff that someone is waiting when a patient enters a hospital room. And on construction sites, the technology can inform the project leaders where workers are located to help guide efficiency.

The company’s funding comes amid the rise of “physical AI,” or an iteration of robots in which advances in sensing, perception and large AI models provide machines with capabilities that traditional automation never supported.

“Earlier robots followed fixed commands and worked only in predictable environments, struggling with the unpredictability found in everyday operations such as shifting layouts, varying item shapes, mixed lighting, and human movement,” PYMNTS wrote last fall. “That is beginning to change as research groups show how simulation, digital twins and multimodal learning pipelines enable robots to learn adaptive behaviors and carry those behaviors into real facilities with minimal retraining.”

One of the clearest examples of physical AI moving from research to frontline use is Amazon’s Vulcan robot, which uses vision and touch to pick and stow items in fulfillment centers, letting it handle flexible fabric storage pods and unpredictable product shapes.

Rival Walmart, meanwhile, is expanding physical AI systems throughout its distribution network, using automation platforms that reduce unit-handling costs and boost throughput.

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