The cry of “atoms, not bits,” a phrase capturing Silicon Valley’s growing obsession with physical manufacturing over digital products, reached a fever pitch last week. This came with word that Jeff Bezos is putting together a one hundred billion dollar fund to roll up and automate factories.
But automating factories is not purely a hardware problem. It increasingly depends on sophisticated software and AI tools, and that shift is reshaping the companies building the infrastructure of the physical manufacturing world. Karthik Gollapudi, the CEO of Sift, an El Segundo, California company whose tools support the design and manufacturing of complex machines like spacecraft and cars, is feeling the ground shift underfoot. He says these changes have reshaped his company’s focus in the last six months.
Gollapudi and his co-founder, CTO Austin Spiegel, started the company in 2022 after working on software tools at SpaceX. Those tools managed the huge amount of telemetry data, which is real-time performance information streamed from sensors on physical components, during testing, manufacturing, and launch. Most companies building advanced machines use off-the-shelf database tools or create their own Python scripts, but Sift saw the opportunity to provide companies with a best-in-class tool. Its customers range from United Launch Alliance, a major US rocket builder, and other defense contractors, to robotics and power grid management startups.
However, Gollapudi says that the arrival of AI tools for data analysis forced a change at his business. The kinds of customized workflows that once stood out as the company’s signature offering have become standard in a world of AI and deep learning models. But the company’s ability to manage data infrastructure had suddenly become more valuable. Gollapudi stated that their long-term vision of how they saw this playing out over five years is actually being realized this year.
That means managing the intense data flow from today’s software-intensive machines. Some vehicles the company works with have more than 1.5 million sensors streaming data concurrently, across multiple formats and time scales. Organizing and storing that data for AI applications is the company’s goal. Gollapudi said a lot of the value is in exposing that data to be machine readable. If AI agents are going to make decisions about manufacturing or analyze test data to flag potential problems, Sift’s goal is to make that data available to them.
Jeff Dexter, the VP of software at Astranis, a satellite company that uses Sift to manage test, manufacturing, and operations, said that good data infrastructure matters for companies like his that might do ten million automated software tests in a day. He explained that it inevitably gets to a point where it costs millions of dollars per month just to store data, and the question becomes whether that is money well spent. With technology like Sift, he said he does not worry about how much data is there.
Gollapudi told TechCrunch that Sift raised a forty-two million dollar Series B round in 2025 at a two hundred seventy-four million dollar post-money valuation. The round was led by StepStone with participation from GV, which is Google’s venture arm, Riot Ventures, Fika Ventures, and CIV.