Two former SpaceX software leaders are taking rocket-grade data systems into mainstream manufacturing. Their startup, Sift Stack, is building the data backbone that lets AI reason over torrents of sensor readings on the factory floor—the same kind of telemetry discipline that kept rockets on schedule and test stands humming.
From Rocket Telemetry to Production Lines
Sift Stack was founded in 2022 in El Segundo, California, by CEO Karthik Gollapudi and CTO Austin Spiegel, who previously engineered the software pipelines that ingested and organized SpaceX’s sprawling test, manufacturing, and launch data. In aerospace, they learned that performance lives in the details: synchronized streams, rigorous metadata, and the ability to replay and compare events across thousands of components under wildly different conditions.
That experience translates directly to next-generation factories. Modern vehicles, turbines, and robots can host well over a million sensors firing concurrently, often at different frequencies and in incompatible formats. Sift Stack’s focus is not on flashy dashboards but on the unglamorous plumbing—collecting, time-aligning, storing, and serving that firehose so engineers and AI agents can actually use it.
The founders say the rise of powerful AI tools has shifted customer priorities. Custom workflows that once differentiated software vendors are now table stakes; what manufacturers struggle with is dependable, scalable data infrastructure. When models need clean, machine-readable histories—years of test results, production quality records, and field telemetry—data engineering becomes mission critical.
The New Backbone for AI in Manufacturing
At its core, Sift Stack tackles three hard problems: heterogeneity, scale, and context. Heterogeneity means blending time-series data from PLCs and lab equipment with log files, images, and test metadata. Scale means ingesting continuous streams from fleets of machines while keeping storage costs in check. Context means attaching rich, queryable meaning—part numbers, revisions, test configurations, operator notes—so AI can connect cause and effect.
That typically involves standards like OPC UA and CAN, columnar formats such as Parquet, and stream processing over Kafka-class event buses. Time alignment across disparate clocks, edge buffering for unreliable networks, and tiered retention policies are table stakes. Compression and downsampling strategies borrowed from hyperscale observability—think Gorilla-like time-series encodings—help control costs without losing signal.
The payoff is practical: If an AI model is going to flag a latent defect after a vibration test or auto-tune a welding robot, it needs synchronized sensor traces, ground-truth labels from quality systems, and a reproducible lineage. McKinsey has reported that predictive maintenance can cut unplanned downtime by 30–50% and reduce maintenance costs meaningfully—results that hinge on data readiness, not just algorithms.
Customers and Real-World Load in Early Deployments
Sift Stack counts aerospace and defense manufacturers among its early adopters, including United Launch Alliance, along with robotics firms and power grid startups. In satellite manufacturing, for example, software teams routinely execute millions of automated tests per day. Leaders at Astranis have described how storage bills for raw test artifacts can climb into the millions per month if left unmanaged—an incentive to adopt infrastructure that tiers data intelligently and keeps only what models and engineers actually need.
Beyond storage, teams want speed. Engineers expect to search across multi-year telemetry in seconds, slice by configuration, and diff two test campaigns as easily as comparing code commits. That is where columnar layouts, vectorized query engines, and well-structured metadata earn their keep. The goal isn’t just faster dashboards; it’s faster root-cause analysis and tighter design-to-production feedback loops.
Why It Matters for the Factory Floor and Beyond
Factories have long relied on MES, PLM, SCADA, and plant historians like OSIsoft PI. But those systems weren’t built for today’s AI workloads or for synchronizing hundreds of data sources across IT and OT domains. Gartner has forecast that most enterprise data will be generated and processed at the edge, a reality already visible in distributed production lines and autonomous equipment. Without a modern telemetry foundation, digital twins stall, traceability suffers, and model performance decays in the wild.
Sift Stack positions itself as the connective tissue between machines, test stands, and data platforms such as Databricks or Snowflake. The emphasis is on interoperability and governance—schema registries, role-based access control, and lineage—so regulated industries can satisfy auditors while moving quickly. For manufacturers navigating supply-chain volatility and electrification, shaving days off failure analysis or accelerating a line ramp can be worth millions.
What to Watch Next in Industrial AI and Data Systems
As industrial AI matures, expect a push toward closed-loop control (models acting directly on equipment), tighter integration with simulation, and robust edge nodes that can run inference inside plants with limited connectivity. NIST and industry groups continue to press for interoperable data models to unlock cross-vendor collaboration, while boards demand measurable ROI and cybersecurity hardening alongside speed.
The throughline is clear: the winners won’t be the flashiest apps, but the teams that make messy, high-volume industrial data reliable and useful. By exporting the discipline of rocket telemetry to everyday production, Sift Stack is betting that the next wave of factory productivity will be written in data pipelines as much as in steel.
