The cry “people, not pieces!” — a phrase that captures Silicon Valley’s growing obsession with physical manufacturing over digital products — reached fever pitch last week as Jeff Bezos raises a $100 billion fund to build and automate factories.
But factory automation is not purely a hardware problem. It is increasingly dependent on sophisticated software and artificial intelligence tools, and this change is reshaping the companies that build the infrastructure of the physical manufacturing world.
Karthik Gollapudi, its CEO Sieve stackan El Segundo, Calif., company whose tools support the design and construction of complex machines like spaceships and cars is feeling the ground shift beneath its feet. He says these changes have reshaped his company’s focus over the past six months.
Gollapudi and his co-founder, CTO Austin Spiegel, started the company in 2022 after working on software tools at SpaceX that managed the vast amount of telemetry data — real-time performance information from sensors on physical components — during testing, manufacturing and launch.
Most companies building advanced machines use off-the-shelf database tools or cook up their own Python scripts, but Sift saw an opportunity to provide companies with a best-in-class tool. Customers range from United Launch Alliance, a major US rocket maker, and other defense contractors to robotics and power grid management startups.
However, Gollapudi says the arrival of AI tools for data analysis has forced a change in his business. The kinds of custom workflows that once stood out as the company’s signature offering have become table stakes in a world of artificial intelligence and deep learning models. But the company’s ability to manage its data infrastructure had suddenly become more valuable.
“Our long-term vision of how we saw this play out over five years is actually being realized this year,” Gollapudi told TechCrunch.
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That means handling the heavy data flow from today’s software-intensive machines. Some vehicles the company works with have more than 1.5 million sensors transmitting data simultaneously, in multiple formats and time scales.
Organizing and storing that data for AI applications is the company’s goal — “most of the value is in exposing it so it’s machine-readable,” Gollapudi said. If AI agents are to make manufacturing decisions or analyze test data to point out potential problems, Sift’s goal is to make that data available to them.
Jeff Dexter, vice president of software at Astranis, a satellite company that uses Sift to manage testing, manufacturing and operations, said good data infrastructure matters to companies like his that might do 10 million automated software tests a day.
“Inevitably, it gets to a point where it’s costing us millions of dollars a month just to store data,” Dexter said. “Does it really feel like a million dollars well spent? With technology like Sift, I’m not worried about how much data there is.”
Gollapudi told TechCrunch that Sift has raised a $42 million series round in 2025 to $274 million post-money valuation, led by StepStone with participation from GV (Google’s business arm), Riot Ventures, Fika Ventures and CIV.
