White Paper: Perfect Match — The Learning Loop
Executive summary
Perfect Match connects predictions to real‑world results. By capturing outcomes at 30/90/180 days and feeding them back into scoring, the system learns which signals actually matter for performance, ramp, and retention. Score explainers keep reviewers in the loop; exports and dashboards ensure the process is auditable and fair. Over time, outcome‑linked learning compounds into a defensible advantage—a proprietary data moat tuned to your context.
- Shortlist quality increases as the model aligns with your outcomes.
- Onboarding and ramp accelerate through better role–candidate matching.
- Early attrition reduces as mis‑matches are filtered earlier.
- Leaders gain visibility into which signals predict success.
Contents
Problem & market context
Traditional screening treats the decision as the endpoint. Without linking decisions to post‑hire outcomes, teams can’t tell which signals truly predict performance or tenure. The result: static rules, drifting accuracy, and missed opportunities to improve matching over time.
Solution overview
Outcome‑linked intelligence
Feedback at 30/90/180 days systematically updates weights so the model reflects your reality, not generic benchmarks.
Score explainers & drill‑downs
Readable rationales and sub‑scores show the evidence behind each recommendation and enable human adjudication.
Feedback hooks
Bidirectional connections with ATS/HCM, performance, and learning systems so outcomes flow back automatically.
Sidecar deployment
Embed as a plug‑in to existing systems; no new portal required to realize value and capture feedback.
Audit & fairness exports
Evidence packs, rationale history, and cohort analysis support compliance reviews and continuous improvement.
Data moat
Longitudinal outcomes create a compounding advantage that is specific to your org, roles, and markets.
Architecture & data flow
Ingestion
- ATS/HCM decisions and interview artifacts
- Performance signals (OKRs, MBOs, manager reviews)
- LMS & enablement (course completions, certifications)
- Engagement & satisfaction (pulse, CSAT/ESAT)
- Optional: CRM/CS metrics for customer‑facing roles
Feature store
- Stable features (experience, skills, domain proximity)
- Context features (team, region, level, product area)
- Outcome features (ramp time, early performance, retention)
Learning services
- Outcome ingestion & label quality checks
- Calibration & reliability analysis
- Weight updates with fairness constraints
- Explainability & rationale templating
- Dashboards & exports for stakeholders
The learning loop (30/90/180)
Define outcomes
Choose objective measures per role family—e.g., time‑to‑ramp, first‑90‑day quality indicators, retention at 180 days.
Collect signals
Pull outcomes via secure APIs; de‑noise with rules (minimum observations, supervisor confirmations, outlier clipping).
Recalibrate
Update weights with safeguards (holdout validation, A/B or shadow testing) before promoting new settings.
Guardrails
- Fairness constraints and cohort monitoring
- Rollback to last known good if drift thresholds trip
- Change logs for rationale templates and thresholds
The loop strengthens over time as more outcomes accumulate, increasing signal‑to‑noise and reducing variance.
Scoring & explainability
Composite design
Role Compatibility combines sub‑scores for experience fit, capability signals, skills coverage, and context fit. Outcome feedback adjusts the relative importance of each component per role family.
Illustrative rationales
RCS 83: “High alignment to AE Enterprise; strong ramp in adjacent market; quota attainment at prior org; minor gap in MEDDICC depth.”
RCS 70: “Solid product chops; strong onboarding trajectory; limited exposure to regulated markets; fit for L5 with mentorship.”
Transparency for reviewers
- Readable reasons + sub‑scores with drill‑downs
- What‑if sliders to test sensitivity to requirements
- Exportable audits (CSV/PDF) with lineage
Outcomes & KPIs
Organizations track early performance, ramp, and retention as Perfect Match calibrates. The ranges below are representative of deployments after calibration; actuals vary by role and baseline.
- Early performance: quality of first 90‑day outputs
- Ramp time: time to target productivity
- Retention: 180‑day attrition
- Experience: candidate and manager satisfaction
Use pilot baselines and monthly dashboards to validate improvement and attribute causality.
Trust, privacy & governance
Explainability
Rationales and sub‑scores are visible and exportable; editor changes are tracked for review.
Responsible AI
Fairness monitoring by cohort, drift detection, and alerts; approvals required before major weight changes.
Privacy
Data minimization, purpose limitation, and retention policies with role‑based access controls.
Adoption plan
- Week 0–2: Connect ATS/HCM, performance, and learning systems; define role families and outcomes.
- Week 2–4: Establish baselines; enable rationale templates; align stakeholders on KPIs.
- Week 4–8: Run pilot with review checkpoints; calibrate weights; monitor fairness metrics.
- Week 8–12: Switch on automated 30/90/180 feedback; promote new weights after holdout validation.
- Week 12+: Scale to more roles; institutionalize dashboards; schedule quarterly governance reviews.
Limitations & risks
- Label noise: Outcome signals can be inconsistent; mitigate with confirmation steps and outlier rules.
- Stationarity drift: Role definitions and markets change; recalibration and monitoring are required.
- Attribution: Improvements may correlate with other changes (training, tooling); use A/B or diff‑in‑diff where possible.
- Privacy: Post‑hire data requires strict access and minimization; document purposes and retention.
Appendix: glossary & references
- Outcome‑linked intelligence
- Learning framework that updates scoring weights from validated post‑hire outcomes.
- RCS (Role Compatibility Score)
- Composite of sub‑scores (experience fit, capability signals, skills coverage, context).
- 30/90/180
- Outcome checkpoints that provide early, mid, and stabilization feedback for calibration.
This document paraphrases publicly available descriptions of Intelletto’s “Perfect Match — The Learning Loop” and organizes them as a formal white paper.