A cry of “Atoms, not bits!” It’s a phrase that describes Silicon Valley’s growing obsession with physical manufacturing over digital products, culminating last week with Jeff Bezos saying he was raising $100 billion to ramp up and automate factories.
But factory automation is not purely a hardware issue. Increasing reliance on sophisticated software and AI tools is reshaping the companies that build the infrastructure of the physical manufacturing world.
Kartik Gollapudi, CEO of Shift Stack, an El Segundo, Calif.-based company that provides tools to help design and build complex machines like spacecraft and cars, sees a shift underway. These changes, he says, have refocused his company over the past six months.
Gollapudi and his co-founder CTO Austin Spiegel founded the company in 2022 after working at SpaceX on software tools to manage vast amounts of telemetry data (real-time performance information streamed from sensors on physical components) during testing, manufacturing, and launches.
While most companies building advanced machines use off-the-shelf database tools or write their own Python scripts, Sift saw an opportunity to provide companies with best-in-class tools. Customers range from leading U.S. rocket manufacturer United Launch Alliance and other defense contractors to robotics and power grid management startups.
However, Gollapudi says the advent of AI tools for data analysis has forced changes in the business. The kind of customized workflows that once stood out as the company’s signature offering have become a key element in the world of AI and deep learning models. But the company’s ability to manage its data infrastructure suddenly became more valuable.
“Our long-term vision of seeing how this plays out over five years is really playing out this year,” Gollapudi told TechCrunch.
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This means managing the massive flow of data from today’s software-intensive machines. Some of the company’s vehicles are equipped with more than 1.5 million sensors that stream data simultaneously across multiple formats and time scales.
The company’s goal is to organize and store that data for AI applications, and “a lot of the value is in exposing the data to be machine readable,” Gollapudi said. Sift’s goal is to make that data available to AI agents, whether they want to make manufacturing decisions or analyze test data to flag potential issues.
Jeff Dexter, vice president of software at Astranis, a satellite company that uses Sift to manage test, manufacturing, and operations, said a good data infrastructure is important for companies like his that may perform 10 million automated software tests a day.
“Inevitably, it’s going to cost millions of dollars a month just to store that data,” Dexter said. “It really feels like $1 million well spent. With technology like Sift, you don’t have to worry about how much data is there.”
Gollapudi told TechCrunch that Sift raised a $42 million Series B at a post-money valuation of $274 million in 2025, led by StepStone with participation from GV (Google’s venture arm), Riot Ventures, Fika Ventures, and CIV.
