‍Sift closes $42M Series B

Sift is building the sensor intelligence platform for some of the most ambitious machines ever built. AI is moving into the physical world and into the product development process, so the constraint is no longer collecting data, it is turning signals into decisions fast enough to matter. The teams that win will build hardware like software with tight loops, reproducible outcomes, and compounding learning across the lifecycle.

In the official announcement, Sift shared that it closed a $42M Series B led by StepStone Group, with GV as its largest investor, bringing total funding to $67M. The company describes the problem clearly: software has tracing, logging, and metrics; hardware still too often runs on CSVs, one-off scripts, and institutional memory. That gap between what the machines know and what engineers can actually see is exactly the problem Sift is built to close.

Sift turns telemetry into ground truth for how machines interact with the physical world. What stands out in the announcement is not just the financing, but the specificity of the operating need: high-frequency sensor streams, multimodal telemetry, long-lived historical test data, and an infrastructure layer that makes all of it queryable and usable by both engineers and models. That is the missing layer for mission-critical hardware.

Sensor intelligence has been the consistent throughline in my career, increasing in importance, complexity, and consequence at every step. I watched sensor data quietly become mission-critical infrastructure for autonomous systems. More sensors, higher data rates, more complexity, and a tighter coupling between data and decision making.

What is different now is that this pattern is no longer isolated. It is becoming the default across robotics, autonomy, aerospace, manufacturing, and simulation. Physical AI systems are built on continuous perception and decision loops, and those loops are only as strong as the quality, context, and speed of the underlying sensor intelligence. This is where the leverage is, and it is pushing to the edge.

The bottleneck is converting raw data into intelligence that can be interrogated and acted on by humans and machines.

This is why I joined Sift. More on that soon.