Question 1

Can we keep our existing engineering tools?

Accepted Answer

Yes. Sift works alongside your acquisition systems, scripts, and dashboards. It integrates with Grafana and LabVIEW, exports to MATLAB, and offers REST and gRPC APIs with Python, Rust, and Go client libraries. Engineers keep the tools they rely on and stop maintaining the glue between them.

Question 2

Can we compare builds and configurations?

Accepted Answer

Yes. Each run is recorded against its robot and configuration. Families align related runs and compute the normal envelope, so a build is compared against earlier builds rather than a single reference.

Question 3

Can we bring in data from local or disconnected systems?

Accepted Answer

Yes. Sift Edge captures and views data beside the hardware, even offline, and syncs selected runs to the shared workspace when connected. Files from disconnected systems can also be imported after the test.

Question 4

Where can Sift run, and how is access controlled?

Accepted Answer

Sift runs as managed cloud, hybrid, in your VPC, on-premises, or air-gapped. Access is set by asset, data set, and user group, and audit logs record activity. Some features, including AI, depend on the environment and its connectivity, so confirm them for yours.

Question 5

Can we import ROS 2 recordings stored in MCAP?

Accepted Answer

Yes. Sift’s MCAP importer supports recordings that use ROS 2 schemas and encoding. Supported file imports also include CSV, Parquet, TDMS, HDF5, and ULog. Other MCAP encodings are not currently decoded, so confirm the recording’s encoding and schema when planning ingestion.

Question 6

Can we start with one robot or test cell?

Accepted Answer

Yes. Most teams start with a structured proof of concept on real data: one representative test workflow, with its formats and review criteria. Sift engineers work alongside your team, and the same setup extends to more cells and builds.

Question 7

How should we measure the value?

Accepted Answer

Measure the workflow you start with: time from test completion to an engineering decision, review hours per run, and the effort spent maintaining data infrastructure. Sift customer stories report results in these terms, from review hours saved per engineer to infrastructure costs avoided.

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