Plant ops, quality, supply chain

Hiring for manufacturing? Here's how we evaluate.

We score how candidates reason through a line problem and hold the safety line under schedule pressure — not whether they can quote the SOP from memory. The model runs the scenario; the rubric is ours.

See the rubric

What we score

The dimensions, not the playbook.

We don't publish the exact criteria, weights, or sub-probes — that's how candidates would game the rubric. Here's what every manufacturing candidate is scored against.

Root-cause reasoning
Whether they trace a defect or downtime event back to its actual cause, or stop at the first plausible explanation. We score the path they take to get there, not just the answer they land on.
Safety-first judgment
Whether they hold the safety line when it costs time, or let schedule pressure talk them into a shortcut. This is scored independently of how technically sound the rest of their answer is.
Cross-shift handoff clarity
How completely they pass a problem to the next shift or the next team — what's still open, what's already been tried, and what to watch for. Gaps here are where real incidents start.
Quality-under-deadline judgment
Whether they hold a quality standard when a deadline is close, or wave something through to hit the number. We score the trade-off they make, not just whether they hit the target.

Sample scenarios

What candidates actually face.

Two illustrative scenario types — the actual prompts vary per session and stay private to your tenant.

Scenario 1
A line stoppage with an unclear root cause.
The AI plays a shift lead reporting a recurring defect with conflicting early signals. We score whether the candidate investigates systematically or jumps to the first plausible fix.
Scenario 2
A schedule conflict between safety and throughput.
Mid-scenario, hitting the shift target requires skipping a safety check that's technically discretionary. We score whether the candidate holds the line, escalates, or quietly lets it slide.

Integrity signals

What we watch for — and what stays private.

We name the signals we capture, but not how we weight or threshold them. That's the part that breaks if we publish it.

  • Every session is recorded — audio, video, and full transcript — and retained per your tenant policy.
  • Every score ships with an ML confidence band. Low-confidence scores are flagged for human review before the candidate is decided on.
  • We evaluate judgment and communication signals only — SkillPlatform doesn't certify safety training, equipment competency, or regulatory qualifications. Those stay with your existing certification process.
  • Admin labeling lets your team flag interviews where the AI's read of a scenario diverged from what a senior plant lead would catch.
  • We never train shared models on your candidate data.

What we measure

The outcome you can defend.

Root-cause-reasoning score, safety-judgment score, and end-to-end completion rate for every candidate — plus a confidence band on each. We measure how often our 'strong hire' candidates clear your plant or ops panel, and we recalibrate when the gap widens. The metric that matters most: the rate at which our 'no hire' signal earns enough trust to skip a full second-round screen.

We frame these as what we measure, not as customer-attributed metrics.

Want to see how this rubric scores a real candidate?

An expert will walk you through a live manufacturing interview transcript — including how the integrity signals played out — in 15 minutes.

See pricing
Other domains:EngineeringHealthcareEnergy
SOC 2 Type II — In progressGDPR-readyTenant-isolated infrastructureData residency: USOngoing rubric consistency reviewNo training on your candidate data