Ex-Apple Engineers Behind $200M Xnor Deal Launch ElastixAI, Secure $16M To Revolutionize AI Inference Across Devices

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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Elite Founders With Track Records at Apple, Waymo, Meta, and AI2

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.

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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Building a Scalable Inference Layer for AI's Next Generation

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.

“We saw a gap when it comes to delivering AI inference at scale and at low cost,” Rastegari told GeekWire.

The startup remains in stealth, but its positioning puts it in conversation with major players like Nvidia (NASDAQ:NVDA), Coreweave (NASDAQ:CRWV), and inference-focused startups such as Fireworks.ai and Together.ai, GeekWire says.

Strong Investor Support In a Crowded AI Infrastructure Race

The $16 million round includes participation from Catapult, Tyche Partners, Liquid 2 Ventures, and DNX Ventures, according to GeekWire.

Cameron Borumand, general partner at FUSE, told GeekWire the firm was "thrilled to back the elite technical founders at Elastix to solve the hardest problems around scaling compute and infrastructure in the fast-growing AI inference market."

The company is also well-placed within Seattle's booming AI scene. With Apple's growing presence in the region and continued investment in foundational technologies, ElastixAI has access to both talent and infrastructure, according to GeekWire.

As AI demand continues to surge, inference platforms are becoming critical infrastructure. ElastixAI's unique combination of flexibility, leadership, and investor confidence may position it as one of the most closely watched stealth startups of the future.

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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.

Don't Miss:

Hasbro, MGM, and Skechers trust this AI marketing firm — Invest before it's too late.
‘Scrolling To UBI' — Deloitte's #1 fastest-growing software company allows users to earn money on their phones. You can invest today for just $0.30/share with a $1000 minimum.

Elite Founders With Track Records at Apple, Waymo, Meta, and AI2

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.

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Building a Scalable Inference Layer for AI's Next Generation

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.

“We saw a gap when it comes to delivering AI inference at scale and at low cost,” Rastegari told GeekWire.