Sift achieves FedRAMP High certification through partnership with Knox Systems. PR Newswire Certification Enables Federal Agencies to Adopt a Unified Platform that Captures, Stores, and Automatically Analyzes Sensor Data to Help Engineers Identify Issues Faster MARINA DEL REY, Calif. and NEW YORK, June 2, 2026 /PRNewswire/ - Sift, the data infrastructure platform for physical AI, today announced it has achieved Federal Risk and Authorization Management Program (FedRAMP) High certification in partnership with Knox Systems (Knox), the largest federal AI-managed cloud provider. FedRAMP is a rigorous and exclusive U.S. government program designed to standardize security assessment and authorization for cloud service offerings and accelerate adoption of government-grade cloud solutions by federal agencies. Built by engineers with first-hand understanding of the demands of mission-critical hardware environments, Sift provides the data infrastructure layer that enables both engineers and AI systems to make sense of the massive volumes of sensor data generated by physical machines. With this authorization, federal agencies now have access to a purpose-built platform for hardware sensor data with automated anomaly detection, real-time data review, and long-term data retention across programs. "At Sift, we are committed to giving engineers the infrastructure they need to build, test, and operate more reliable hardware systems," said Austin Spiegel, CEO and Co-Founder of Sift. "Too often, engineering teams generate massive amounts of data but must rely on manual workflows, fragmented tools, and legacy systems that break down at scale. Our FedRAMP certification means that engineers working on national security missions now have access to a unified platform that is already proven at scale. Customers already use Sift inside air-gapped environments to handle millions of concurrent sensors across multiple formats and time scales, for both development and live ops. We're excited to bring these services to federal agencies." Knox eliminated the complexity of the FedRAMP authorization process, allowing Sift to obtain FedRAMP High certification within a matter of months while keeping valuable engineering resources focused on product development. As a result, Sift can expand into national security and defense programs and provide federal agencies with access to a proven data platform to support mission-critical hardware environments. "Knox Systems and Sift are cut from similar cloths - we're both delivering solutions based on the challenges that we experienced first-hand," said Irina Denisenko, CEO of Knox Systems. "After partnering with Knox to obtain FedRAMP High certification, Sift can now help national security customers automatically identify anomalies, root-cause issues, and build faster."

Sift, a data infrastructure platform for physical AI, has named co-founder Austin Spiegel as chief executive officer and opened a second office in San Francisco. Co-founder Karthik Gollapudi will focus on product vision and industry evangelism. The company has added six senior leaders from Meta, Anduril, Applied Intuition, Grafana Labs, SpaceX and Palantir, expanding its team to 80 people. Sift's platform ingests up to 20 million data points per second across aerospace, defence, autonomy, rail and advanced manufacturing programmes. Since 2022, Sift has tripled revenue year over year and raised $67 million in total funding. The platform supports companies including Impulse, K2 Space and Parallel Systems, handling mission-critical systems that must work correctly on first deployment.

Sift raises $42M to build the missing data layer for Physical AI. Sift has raised a $42 million Series B led by StepStone, with participation from GV, Riot Ventures, Fika Ventures, and CIV, bringing total funding to $67 million. The company is targeting a growing gap in AI infrastructure: the ability to make physical machines understandable to AI systems. As AI moves beyond software into real-world environments, systems like rockets, satellites, and autonomous vehicles generate millions of sensor data points per second across audio, video, logs, and telemetry. But unlike software environments, this data is largely unstructured and difficult to interpret. Sift's platform aims to solve this by transforming raw machine data into structured, queryable formats that both engineers and AI models can use. In effect, it acts as an "observability layer" for hardware, bringing a level of visibility that software systems have developed over the past two decades. Founded by former SpaceX engineers, the company is already working with organizations such as ULA, Astranis, and K2 Space, supporting systems that operate at fleet scale rather than as isolated machines. The shift from managing single assets to operating constellations of hundreds or thousands of systems is driving demand for automation in monitoring, anomaly detection, and performance validation. With the new funding, Sift plans to expand its engineering team and platform capabilities as more industries move toward AI-controlled hardware systems across defense, space, manufacturing, and autonomy. TheMarketAI take. TheMarketAI has written before that physical AI is fundamentally different from software AI. Large language models benefit from abundant, structured data. Physical systems do not. Instead, they produce messy, high-frequency, multi-modal data that is difficult to interpret and even harder to scale. Sift is tackling a less visible but critical layer of that problem: making the physical world legible to AI. Before AI can control machines, it needs to understand them. That requires translating raw sensor output into structured data pipelines - something that, until now, has largely been handled manually or through fragmented tools. This reinforces a broader theme in Physical AI: progress may not come from better models alone, but from infrastructure that bridges the gap between real-world complexity and machine understanding. The physical part of AI remains both the hardest and most interesting frontier. Companies like Sift are betting that whoever builds the data layer wins.