Paula Tudoran
4 min read
In This Article:
Seattle-based ElastixAI, founded just months ago by veteran engineers behind Apple's (NASDAQ:AAPL) $200 million acquisition of Xnor, has raised $16 million from top-tier investors, including Bellevue-based capital venture company FUSE.
The stealth-mode startup with elite Apple pedigree is quietly tackling one of the most expensive pain points in artificial intelligence deployment: inference, GeekWire reports.
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The founding team behind ElastixAI is no stranger to cutting-edge AI. According to GeekWire, CEO Mohammad Rastegari was co-founder and chief technology officer of Xnor, which was acquired by Apple in 2020 for its groundbreaking edge-based AI tools.
He spent four years at Apple following the acquisition and most recently served as a distinguished scientist at Meta (NASDAQ:META). Rastegari is also an affiliate assistant professor at the University of Washington and spent five years at the Allen Institute for AI, co-founded by the late Microsoft (NASDAQ:MSFT) visionary Paul Allen, GeekWire says.
Chief technology officer Saman Naderiparizi, who led hardware engineering at Xnor, was also a senior engineering manager at Apple. He's joined by third co-founder Mahyar Najibi, a former Apple engineer who also spent time at Waymo, Google's self-driving car project, GeekWire reports.
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While training AI models gets most of the headlines, inference is where the real-world costs pile up. According to TechTarget, every time a chatbot generates or replies to a question, a recommendation system suggests a new item or a smart device reacts to a real-world prompt, the model is performing inference. These post-deployment processes happen at scale and in real time, often thousands or millions of times a day. That volume drives up compute costs, latency concerns, and energy consumption.
GeekWire says that ElastixAI is focused on flexibility and configurability, giving enterprises and hyperscalers the ability to tune their inference infrastructure to specific needs. Whether running on edge devices or large cloud environments, the company's software-centric platform is designed to reduce both compute load and operational cost.
