Sift for Data centers

Qualify every node.

Hand over with evidence.

Connect burn-in, qualification, and commissioning data across nodes, racks, power, and cooling. Investigate failed nodes against their peers, and hand hardware over with the evidence behind every check.

Resolve burn-in issues.

Keep deployments moving.

Bring burn-in results, node configurations, logs, and facility data into one shared record for the next acceptance decision.

Why is one node running hotter than its peers?

Node 14 ran 8 °C above its rack during burn-in. Compare it with its peers, inspect the cooling path, and share the finding.

Keep qualification history, engineering findings, and review evidence connected as hardware moves through burn-in, commissioning, and operations. Build on what’s already known and bring operational learning back into qualification.

Equipment qualification

Establish how equipment behaves and keep the evidence for later burn-in.

Burn-in

Compare nodes and racks with qualification results and configuration history in reach.

Commissioning

Bring checks, comparisons, and open issues into the acceptance review.

Operations

Investigate failures with the hardware’s history available, and bring findings back into qualification.

Operational findings inform qualification

For engineers

Get to answers faster.

For engineering leaders

Keep teams moving together.

For executives

See what’s ready.

Know what’s at risk.

Investigate failed nodes with earlier burn-in results and configuration history in reach. Reuse useful analysis and carry findings into the next rack.

Standardize acceptance analysis, share findings across hardware, facilities, and operations teams, and keep continuity from burn-in to handover.

Bring qualification, commissioning, and acceptance results with open issues into deployment reviews, so teams can prioritize the work that keeps deployments on schedule.

Proven on complex hardware programs.

These results come from adjacent engineering fields: spacecraft, autonomous rail, and manufacturing. They are not data-center outcomes.

Fits your stack.

Grows with your program.

Can we keep our existing engineering tools?

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.

Can we compare a node with its rack and earlier deployments?

Yes. Each burn-in run is recorded against its node and rack. Families align related runs and compute the normal envelope, so a node is compared against its peers and earlier racks rather than a single reference.

Can we bring in data from local or disconnected systems?

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.

Where can Sift run, and how is access controlled?

Sift runs as cloud SaaS, hybrid, or fully on-premises, including air-gapped environments, with the same capabilities in each. Access is set by asset, data set, and user group, and an audit log records every query. Sift Edge captures and views data beside the hardware, even offline.

Can we start with one rack or one burn-in workflow?

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

How should we measure the value?

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.

Accelerate your hardware development

Start with the engineering question slowing your team down.

Bring a representative workflow, your data requirements, and the decision you need to make. Work with Sift to identify a useful starting point and how to evaluate it.

Talk to a Sift engineer

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