Leadify — AI leadership simulation platform
The Leadify evaluation engine

The science of measuring behavior.

Every Leadify score comes from a calibrated psychometric engine, not an LLM's opinion. This page explains how.

Book a methodology reviewSee the four pillars
Evaluation engine · v3.2 calibrated
  • Live conversationsignal in
  • IRT-calibrated rubricβ = 1.24
  • Bayesian updateθ = 0.72
  • Verified recordSEM ±0.11
Philosophy

We measure what people do, not what they say they'd do.

Behavior sample · N = 1
θ posterior 0.7295% CI [0.61, 0.83]
Four pillars

How the engine turns conversation into evidence.

Pillar 01

Behavioral Elicitation

Scenarios are engineered to elicit specific competencies under realistic pressure. Each is co-designed with veteran operators and mapped to a defined competency model, so every conversational beat has a purpose.

Competency-mappedAdaptive pressureOperator-designed
Pillar 02

Calibrated Scoring (IRT)

Scores are calibrated using Item Response Theory — the same psychometric framework behind the GRE and GMAT. Every scenario carries a difficulty parameter (β) and discrimination parameter (α), so a 75 in a hard scenario means more than a 75 in an easy one.

β difficultyα discriminationCross-scenario comparable
Pillar 03

Bayesian Ability Estimation

One conversation is a sample, not a verdict. Leadify uses Bayesian updating: each session refines an ability estimate (θ) with an explicit confidence interval. We show the score and how certain we are — certainty grows with evidence.

θ estimateCredible intervalsEvidence-weighted
Pillar 04

Continuous Validation

The engine is back-tested against real outcomes: promotion decisions, retention, performance ratings, revenue attainment. Scenario parameters are recalibrated as the behavioral dataset grows.

Outcome-linkedRolling recalibrationAdverse-impact monitored
Scoring pipeline

From live conversation to verified behavioral record.

  1. Step 01

    Live conversation

    Participant engages an AI counterpart in a realistic, adaptive scenario.

  2. Step 02

    Behavioral signal extraction

    Verbal and paraverbal markers are captured from the transcript and audio.

  3. Step 03

    Rubric scoring

    Multi-dimensional, calibrated rubrics score observable behavior, not self-report.

  4. Step 04

    IRT adjustment

    Difficulty and discrimination parameters weight the score against the scenario.

  5. Step 05

    Bayesian update

    Ability estimate (θ) is refined with an explicit confidence interval.

  6. Step 06

    Verified behavioral record

    Every score traces back to the specific conversational evidence that produced it.

Verified behavioral data

What "verified" actually means.

Every score is a claim we can defend — to a CHRO, a works council, or an auditor.

0.00
Predictive validity (r)
0%
Inter-rater agreement
0%
Auditable to evidence

  • Observed, not self-reported — every data point comes from live behavior.

  • Standardized — same scenario, same counterpart, same rubric for everyone.

  • Calibrated — difficulty-adjusted so scores are comparable.

  • Bounded — every score carries an explicit confidence interval.

  • Auditable — every score traces back to specific conversational evidence.

Standards

Built to professional assessment standards.

Designed in alignment with the Standards for Educational and Psychological Testing (AERA, APA, NCME) and SIOP's Principles for the Validation and Use of Personnel Selection Procedures.

APA StandardsSIOP PrinciplesGDPRKVKKSOC 2 (in progress)
Selected research foundations
  • Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology. Psychological Bulletin, 124(2), 262–274.
  • Sackett, P. R., Zhang, C., Berry, C. M., & Lievens, F. (2022). Revisiting meta-analytic estimates of validity in personnel selection. Journal of Applied Psychology, 107(11), 2040–2068.
  • Lord, F. M. (1980). Applications of Item Response Theory to Practical Testing Problems. Lawrence Erlbaum Associates.
  • Arthur, W., Day, E. A., McNelly, T. L., & Edens, P. S. (2003). A meta-analysis of the criterion-related validity of assessment center dimensions. Personnel Psychology, 56(1), 125–153.
  • Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.
  • Phillips, J. J. (2003). Return on Investment in Training and Performance Improvement Programs (2nd ed.). Butterworth-Heinemann.
FAQ

Answers, straight.

Traditional assessment centers deliver high predictive validity but cost $3,000–8,000 per person and run once a year. Leadify delivers the same class of behavioral evidence through AI simulation — on demand, at a fraction of the cost, with psychometric calibration built in.
No. Leadify produces behavioral evidence with confidence intervals. Humans make decisions; Leadify makes those decisions better-informed.
Every rubric is calibrated against expert human raters before deployment, and inter-rater reliability is monitored continuously. Scores carry explicit confidence intervals that widen when evidence is thin and tighten as more sessions are observed. Human raters audit a random sample of every scenario batch and disagreements feed back into rubric recalibration.
Simulation-based assessment measures how a person behaves in a realistic reconstruction of a high-stakes situation, rather than what they claim they would do on a self-report questionnaire. It is the modern, scalable form of the assessment center tradition — observable behavior, scored against a calibrated rubric.
Every scenario and rubric is monitored for adverse impact across demographic groups. Calibration data, rater agreement, and score distributions are reviewed on a rolling basis, and any item flagged for differential functioning is recalibrated or retired. Every score is auditable back to the specific conversational evidence that produced it.
BoundariesEvaluation Engine · Calibrated · v2.13

What the Evaluation Engine does not do.

The most defensible assessment vendors are the ones who name their limits. Every claim below is a claim Leadify refuses to make.

  • We don't make hiring decisions.

    Humans do. Leadify produces behavioral evidence — the promotion, hire, or succession call belongs to the accountable decision-maker.

  • We don't score personality.

    We score observable behavior under standardized pressure. No inferred traits, no personality typologies, no black-box archetypes.

  • We don't claim certainty we don't have.

    Every score carries its confidence interval. Where evidence is thin, the interval widens — and the report says so, in the same numeric language finance and the board already read.

Enterprise dossier

Want the full technical documentation?

Our team walks your I/O psychology and procurement teams through the engine — calibration data, validation studies, fairness monitoring, and audit trails.

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