Filter out the noise. Understand how classification thresholds organize chaos into structured, high-precision hiring decisions.
Candidates with an AI Score ≥ threshold are Shortlisted. Others are Rejected.
Slide the threshold to see instant trade-offs, or select a Hiring Preset below to apply standard operational profiles.
Instantly trigger typical business trade-offs with custom thresholds.
Click a scenario to put theoretical concepts into concrete operational decisions.
Your recruiting team has infinite time to screen candidates. You want to make absolutely sure you do not miss out on stellar talent. What should you optimize for?
You are building a startup. Your developers are swamped writing code and only have bandwidth to interview exactly 2 candidates. What should you optimize for?
Quality Focus: Out of all candidates your AI shortlisted, what % are actually qualified? High precision avoids wasting recruiter time on bad interviews.
Quantity Focus: Out of all qualified talent in the pool, what % did the AI catch? High recall prevents missing out on star candidates.
The Balance: Combines Precision and Recall. It penalizes extreme imbalances (e.g., if you shortlist everyone or no one).
Overall Right: What % of all system decisions (Shortlists + Rejections) were completely correct? Can be misleading if dataset is unbalanced.
Qualified Person Shortlisted
Unqualified Person Shortlisted
Qualified Person Rejected
Unqualified Person Rejected
| Candidate | True Skill | AI Score | System Decision |
|---|