Sift raises $42 million in Series B round

2026-03-25 · Source: artificial intelligence Archives - SpaceNews · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Fundamental Awareness, quick

Summary

Sift, a Southern California startup, secured $42 million in a Series B investment round, led by StepStone Group with participation from Google Ventures, Riot Ventures, Fika Ventures, and CIV. Founded in 2022 by former SpaceX leads Karthik Gollapudi and Austin Spiegel, Sift develops tools to structure raw hardware sensor data for engineers and AI systems. The company plans to use the funding to expand its engineering staff and build infrastructure for AI-controlled hardware, addressing the challenge of interpreting vast amounts of data generated by modern rockets, satellites, defense systems, and autonomous vehicles. Sift aims to provide an intelligence layer that enables AI to interact fluently with hardware, moving beyond traditional spreadsheet-based analysis.

Key takeaway

For Directors of AI/ML overseeing hardware-intensive projects, Sift's approach to structuring sensor data offers a critical solution for integrating AI control. Your teams can move beyond manual data analysis, enabling AI systems to interpret complex telemetry from devices like satellites and autonomous vehicles. Consider evaluating Sift's platform to streamline data pipelines and enhance AI's operational fluency with physical hardware.

Key insights

Sift structures raw hardware sensor data for AI-controlled systems, enabling fluent interaction between AI and physical devices.

Principles

• AI requires structured data for hardware interpretation.
• Hardware observability lags software observability.
• Real-world data improves design loops.

Method

Sift automatically transforms raw sensor data into structured, queryable data, facilitating interpretation by both engineers and AI systems, and flags out-of-bounds telemetry.

In practice

• Automate sensor data structuring.
• Integrate AI with hardware telemetry.
• Use real-world data for design improvements.

Topics

• Hardware Sensor Data
• AI-Controlled Hardware
• Data Transformation
• Observability Infrastructure
• Satellite Telemetry

Best for: VP of Engineering/Data, Director of AI/ML, AI Architect, AI Engineer, CTO, Investor

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Editorial summary, takeaway, and curation by AIssential. Original article published by artificial intelligence Archives - SpaceNews.