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 a vehicle with the fleet and earlier builds?

Accepted Answer

Yes. Each run is recorded against its vehicle and configuration. Families align related runs and compute the normal envelope, so a vehicle is compared against its peers and earlier builds rather than a single reference. Runs can be aligned to their start, end, a chosen timestamp, or an annotated event such as the beginning of a duty cycle. Event-based alignment uses a matching annotation in each run, allowing tests recorded at different times to be compared on a shared relative timeline.

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 start with one vehicle program?

Accepted Answer

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

Question 6

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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