Sift

K2 Space generates nearly 20 million data points per second during hardware tests. As their production line scaled, standard time-series stacks buckled under the cardinality, latency, and schema flexibility required to keep up. They needed a telemetry architecture that could ingest at that volume while detecting anomalies in real time on safety-critical assets. Sift handled all three constraints without trading one off against another. See how K2 Space solved the tens-of-millions-point problem.

Astrolab builds the lunar terrain vehicle for NASA's Artemis program. As their test campaigns scaled, manual data review compounded -- engineers spending thousands of hours chasing anomalies through fragmented tools instead of advancing the vehicle. On Sift, Astrolab recovered 5,000 hours of development time and more than $100,000 in engineering cost. Their team now closes the design loop using real test data, with anomaly detection running automatically across every channel. See how they did it.

Most hard tech teams start with the same stack: a time-series database, a few custom scripts, and a dashboard someone built three engineers ago. It works until it does not. Then maintaining it quietly becomes a second job. Postgres was not built for high-frequency machine telemetry. InfluxDB was not built for anomaly detection at scale. The tools that make sense for a prototype do not survive contact with a real program. Read why the best telemetry infrastructure for hardware teams is one they did not have to build.

Parallel Systems builds autonomous freight vehicles. As their fleet scaled, the cost of maintaining custom telemetry infrastructure became the rate-limiter on the program. Engineering time that should have gone into the product went into keeping the data stack running. After moving to Sift: 85% infrastructure cost reduction, $140,000 saved annually, and 150 hours per engineer per year recovered. The numbers are not from a large company with a dedicated data team. They are from a lean hardware team that decided building their own stack was no longer worth it. See how they made the switch.

Legacy time-series databases were built for web applications, not aerospace telemetry at 10kHz. When compute and storage are tightly coupled, scaling one means scaling both. As your test cadence grows, you provision for worst-case load and pay for it every day. Sift decouples compute from storage entirely, built on object-based architecture so your telemetry infrastructure scales with your program. Read why the best database for flight hardware is no traditional database at all.

Every log parsed by hand is time your engineers are not spending on the vehicle. As test campaigns grow in scale and frequency, manual data review becomes the rate-limiter on your entire program. Sift automates review with rule-based checks, real-time annotations, and auto-generated reports. Your team spends less time chasing anomalies and more time closing out test campaigns. See how aerospace teams eliminate the manual review bottleneck.