AI scoring at interview time correlates with on-the-job performance at 6 months at r = 0.74 (Pearson, 2,400-hire benchmark). Per-customer model retrains on your hiring decisions and performance reviews — gets sharper for your roles over time.
Most hiring teams flying blind: they have no idea whether their interview process actually predicts who succeeds. HRBlade closes that loop. Every hire's interview score is matched against later performance reviews, and the model gets calibrated to your specific roles.
We track AI interview score vs. 6-month manager rating per role family. You see exactly how predictive your hiring is — sortable by role, recruiter and team.
Your model is trained on your hiring decisions and your performance outcomes. After ~50 closed-loop hires, the predictive accuracy on your specific roles outperforms the global benchmark by 12–18%.
After a hire, predict probability of departure within 12 months from interview signals + first 30 days. Surface high-risk hires for proactive manager intervention.
Same model identifies internal candidates ready for promotion based on competency and assessment evolution. Talent intelligence beyond just hiring.
Continuous monitoring of pass-rate and performance correlations across protected groups. Adverse impact alerts within 48 hours of statistical detection.
For every hire, the model surfaces which interview signals were most predictive. Refines your interview process: cut signals that don't predict, double-down on those that do.
Every interview gets a per-competency AI score with confidence intervals. Stored alongside the hiring decision.
At 3, 6 and 12 months, managers grade actual performance. The system matches scores against AI predictions automatically.
Every closed-loop hire updates the model. After ~50 hires per role family, predictions for new candidates in that family beat the global benchmark.
Source: Benchmark cohort: 2,400 hires with verified reviews, 2025
Source: Per-customer model retraining benchmark, 2025
Source: HRBlade compliance commitment, 2025
Discover that your "culture fit" signal predicts nothing while "problem decomposition" predicts everything. Restructure the interview around what actually works.
Compare per-recruiter hire performance: who's calling it right, who's biased toward certain backgrounds. Coach with data, not anecdote.
High-risk new hires get proactive 30/60/90 check-ins from their manager. Reduce first-year attrition by 18% on customer benchmark.
It can't be — manager ratings always have noise. We use multiple signals (peer review, OKRs, retention) and model-aggregate them. We're explicit about the uncertainty: predictions come with confidence intervals, not point estimates.
Multi-agent AI simulates real team dynamics — before you hire, promote, or reorganize.
9 adaptive cognitive games. 95+ calibrated tasks, psychometric backbone, 10 minutes vs. an hour.
Catch flight risk, burnout, and quiet conflict — with evidence quotes from open-text responses.
