One grading engine, many formats.
Every assessment type works the same way: candidates answer async, the AI grades each answer against a rubric you define, and you confirm the decision. Four formats today, plus AI Debugging Challenge and the Hiring Policy rules that route and notify around them. New to the term? What is AI-graded assessment?
Assessment Types
Knowledge (MCQ)
Test the fundamentals a role depends on — multiple-choice, auto-scored, AI-checked.
System Design
Candidates diagram the system and explain it — graded together against your rubric.
Behavioral
See how someone has actually operated — structured answers, graded on the signals you set.
Culture Fit
Check working-style alignment against real behaviors — not a vibe check.
AI Debugging Challenge
Watch how a candidate works with AI to fix real bugs — not whether they used it
Why the score is legible
Every score traces back to the rubric it was graded against — which criteria it hit, which it missed, and why. It's a number for a reviewer to weigh, not a verdict handed down with no way to check its work.
You write the rubric up front. The AI grades each answer against those exact criteria and shows its reasoning alongside the score, so a reviewer can see how the AI got there before they confirm it — the same rubric, applied the same way, every time.
Hiring Policy
AutomationAssessments produce the signal. Hiring Policy acts on it — rules that route candidates and send emails the moment an assessment is graded, with people kept in the loop on any decision that affects a candidate.
Recruitment automation
Define if-this-then-that rules that route candidates to the next assessment or a reviewer and send automated emails as events occur. Consequential, candidate-affecting actions are held for a human decision and never auto-executed — and everything is written to an execution log.
AI grades, you decide.
We're onboarding our first hiring teams. See these assessment types running on your own roles.