Behavioral
Get past the rehearsed answer. Structured, situation-based responses graded on the signals you care about — ownership, collaboration, outcome.
Assessment · in progress
Section 01 · Ownership
18:05Tell us about a time you took ownership of a problem that wasn't strictly yours to fix. What was the situation, what did you do, and what was the result?
Answer guidance
- Situation
- Set the context and the stakes.
- Task
- What was your responsibility here?
- Action
- What did you specifically do?
- Result
- What changed because of it?
Your answer
Saved as you go · graded against your rubric after you submit
The candidate’s view of this assessment. Sample content shown.
How this assessment works
What it assesses
How a candidate has actually operated — ownership, collaboration, and how they handle friction — drawn from concrete past situations rather than hypotheticals.
What the candidate does
Answers a behavioural prompt asynchronously in writing. Optional STAR prompts (Situation, Task, Action, Result) help them structure a complete, specific answer.
How it's AI-graded
The AI reads the response against the rubric you defined, checking for the signals you care about — specificity, ownership, outcome — and marks each criterion with the evidence it found. You confirm the read before it informs a decision.
Assessment report · AI-graded result
AI grade
AI generated0 / 10088% confidence
Clear ownership with a concrete outcome; scope of impact could be quantified.
Scoring rubric
Why this score
The answer follows a complete situation-to-result arc and shows genuine initiative on a problem outside the candidate's remit. The action is specific and the result is real. Impact is described qualitatively rather than measured.
The result, with its reasoning
The AI grades the answer against your rubric and shows its work: a score out of 100, a confidence read, which criteria were met, partial, or not shown, and a short explanation of why. Every part of this is model-assisted and clearly labelled — the hiring call stays with your team.
How grading works
You define the rubric
Set the criteria that count for this role. The rubric is frozen for the run so every candidate is graded the same way.
The AI grades against it
Each answer is graded criterion by criterion, with the evidence the model drew from and a confidence score — never a black-box number.
You confirm the decision
The score is decision support, not the decision. A person reviews the reasoning and confirms it before it counts.
Built to be trusted
Audit trail
Every grade, override, and confirmation is recorded immutably — who, what, and when.
Your data, walled off
Your rubrics, questions, and candidate data stay scoped to your workspace.
GDPR-ready
Soft-delete by default and candidate anonymization when you need it.
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