EL SEGUNDO, Calif., March 25, 2026 – The shift from digital products to physical manufacturing, often summarized by the Silicon Valley mantra “atoms, not bits,” is accelerating. This movement gained significant momentum with recent reports of major investments aimed at automating factories. However, the transformation of manufacturing floors depends as much on sophisticated software as on hardware. Sift Stack, a company founded by two former SpaceX engineers, is at the forefront of this shift, applying rocket-grade data management tools to industrial manufacturing.
Sift Stack Brings Space-Grade Software to Earth
Karthik Gollapudi, CEO of Sift Stack, and his co-founder, CTO Austin Spiegel, launched the company in 2022. Their experience came directly from developing software tools at SpaceX. These tools managed immense volumes of telemetry data—real-time performance information streamed from thousands of sensors on rockets during testing, manufacturing, and launch. Consequently, they identified a critical gap in the market for complex machine manufacturing.
Most companies building advanced machines, from satellites to electric vehicles, traditionally rely on off-the-shelf database solutions or custom Python scripts. Sift Stack recognized an opportunity to provide a specialized, best-in-class platform. The company’s tools now support the entire design and manufacturing lifecycle for complex machinery.
Their client base reflects this broad industrial application. It includes major aerospace entities like United Launch Alliance, various defense contractors, and innovative startups in robotics and power grid management. The common thread is a need to handle vast, heterogeneous data streams.
The Data Deluge in Modern Manufacturing
Modern manufacturing generates staggering amounts of data. Some vehicles Sift Stack works with feature over 1.5 million sensors streaming information concurrently. This data arrives in multiple formats and across different time scales. Managing this flow is not merely a storage issue. It is a fundamental requirement for efficiency, quality control, and innovation.
Jeff Dexter, Vice President of Software at satellite company Astranis, explained the scale. “Companies like ours might run 10 million automated software tests in a single day,” Dexter said. “The data storage costs alone can reach millions of dollars monthly. The key question becomes whether that spending is effective. With technology like Sift, we can focus on value, not just volume.”
The AI Pivot Reshaping Industrial Software
Gollapudi noted that the rapid advancement of AI tools for data analysis forced a strategic evolution at Sift Stack. Previously, the company’s value centered on creating customized data workflows. Now, such customization has become a baseline expectation in an era dominated by AI and deep learning models.
This shift elevated the importance of robust data infrastructure. “Our long-term vision for a five-year timeline is materializing this year,” Gollapudi told TechCrunch. The core mission has crystallized: organizing and storing immense data flows to make them “machine readable” for AI applications.
If AI agents are to analyze test data, flag potential failures, or optimize manufacturing processes, they require clean, accessible, and well-structured data. Sift Stack’s goal is to provide that foundational layer. The company’s recent $42 million Series B funding round, led by StepStone in 2025 with participation from GV, Riot Ventures, Fika Ventures, and CIV, underscores investor confidence in this direction. The round valued the company at $274 million post-money.
Beyond Storage: Enabling Intelligent Decision-Making
The value proposition extends far beyond simple data warehousing. The real opportunity lies in exposing data for real-time analysis and decision-making. This capability is crucial for predictive maintenance, quality assurance, and supply chain optimization.
For instance, anomalies in sensor data during a test cycle can predict component failure long before it happens. AI models trained on historical manufacturing data can suggest design improvements. Sift Stack’s platform aims to be the conduit that makes these advanced applications possible by solving the fundamental data infrastructure challenge first.
The Broader Industrial Automation Landscape
Sift Stack’s journey reflects a larger trend in the technology and manufacturing sectors. The convergence of physical engineering with advanced software and AI is creating a new industrial paradigm. This shift demands tools that can bridge the gap between the physical and digital worlds.
The industry’s move toward greater automation and data-driven operation is not just about replacing human labor with robots. It is about creating interconnected systems where every component’s performance is monitored, analyzed, and optimized continuously. This requires a software backbone capable of handling the scale, speed, and complexity of modern industrial operations.
Key challenges in this space include:
- Data Heterogeneity: Integrating information from legacy systems, modern IoT sensors, and proprietary formats.
- Real-Time Processing: Enabling immediate insights from streaming data to support operational decisions.
- Scalability: Architecting systems that can grow with increasing sensor counts and data velocity.
- Security: Protecting critical industrial data and systems from cyber threats.
The Expertise Behind the Technology
The founders’ backgrounds at SpaceX provide significant credibility. SpaceX’s approach to manufacturing—characterized by rapid iteration, vertical integration, and heavy reliance on data—has become a model for other industries. Applying lessons from the extreme demands of aerospace to broader manufacturing problems represents a compelling transfer of expertise.
This experience-driven approach aligns with Google’s E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) principles for quality content. The founders have direct, hands-on experience solving analogous high-stakes data problems in a premier engineering organization.
Conclusion
Sift Stack exemplifies the critical software layer enabling the next wave of industrial automation. By applying data infrastructure principles proven in the demanding environment of rocket launches, the company is helping diverse manufacturers harness their data for AI-driven gains. As the “atoms, not bits” trend accelerates, the success of physical automation will increasingly depend on sophisticated software platforms like Sift Stack that can manage complexity, enable intelligence, and scale with the industrial data explosion. The company’s trajectory from a SpaceX spin-out to a well-funded industry player highlights the growing convergence of aerospace-grade engineering and mainstream manufacturing innovation.
FAQs
Q1: What is Sift Stack’s primary function?
Sift Stack provides specialized software to manage the massive, real-time sensor data generated during the design, testing, and manufacturing of complex machines like rockets, satellites, and vehicles, making this data usable for AI analysis.
Q2: How did the founders’ experience at SpaceX influence the company?
Founders Karthik Gollapudi and Austin Spiegel worked on software managing telemetry data for SpaceX rockets. This experience solving extreme data infrastructure problems directly informed Sift Stack’s platform, built for high-volume, heterogeneous industrial data streams.
Q3: Why is data infrastructure so important for AI in manufacturing?
AI and machine learning models require clean, well-organized, and accessible data to function effectively. Sift Stack focuses on this foundational layer—ingesting, storing, and structuring data from millions of sensors—so AI tools can perform analysis, prediction, and optimization tasks.
Q4: What types of companies use Sift Stack’s software?
Customers range from major aerospace and defense contractors like United Launch Alliance to startups in robotics, satellite technology (e.g., Astranis), and power grid management, all of which build complex, sensor-heavy machinery.
Q5: How did the rise of AI change Sift Stack’s business focus?
The proliferation of AI tools made customized data workflows a common expectation. This shifted Sift Stack’s competitive advantage toward its core strength: building and managing the scalable, high-performance data infrastructure that AI applications require to deliver value in industrial settings.
Updated insights and analysis added for better clarity.
This article was produced with AI assistance and reviewed by our editorial team for accuracy and quality.
