White Paper: AVAR — AI‑Powered Virtual Automated Recruitment
Executive summary
AVAR (AI‑Powered Virtual Automated Recruitment) turns high‑volume, low‑signal hiring into a transparent, engaging, and evidence‑driven process. It combines video résumés, adaptive technical assessments, and behavioral simulation interviews with a unified, explainable Composite Fit Score. Recruiters work from a command center that ranks candidates, exposes rationale, and captures human approvals; candidates experience a modern portal with clarity and feedback. Post‑hire outcomes at 30/90/180 days close the loop so the system improves over time.
- ~30% faster time‑to‑hire
- ~2× recruiter efficiency
- ~40% improvement in quality‑of‑hire indicators
- ~12‑month breakeven, with compounding gains thereafter
Contents
Problem & market context
Traditional recruitment burdens everyone: candidates fall into black holes, recruiters drown in repetitive triage, and executives lack a real‑time view of pipeline quality. Time‑to‑hire inflates, quality is inconsistent, and fairness is hard to prove. Organizations need a system that moves faster, raises signal, and documents decisions end‑to‑end.
Solution overview
Video Résumé Capture
Candidates introduce themselves via short video; recruiters gain early signal on communication and presence alongside transcripts and highlights.
Adaptive Technical Assessments
Role‑specific tasks that adapt to candidate performance verify what candidates can do, not just what they claim.
Behavioral Simulation Interviews
Realistic scenarios surface leadership, empathy, and problem‑solving under pressure.
Composite Fit Score
Unified, explainable score across skills, behaviors, and experience alignment to prioritize interviews with clarity.
Recruiter Command Center
Ranked queues, drill‑downs, bulk actions, and one‑click scheduling to handle volume without losing quality.
Human‑in‑the‑Loop & Audit
Human approvals, immutable action logs, and bias/drift monitors ensure accountability and fairness.
Architecture & data flow
Ingestion & orchestration
- Candidate intake (resume files, links, referrals) with consent capture
- Video capture & speech‑to‑text transcription
- Assessment delivery & telemetry collection
- Event bus for status changes and webhooks
Processing
- Feature extraction from video, text, and assessment signals
- Bias‑aware normalization and redaction of sensitive fields
- Model ensemble for composite fit scoring
Serving & integration
- Command center UI for recruiters (ranked queues, drill‑downs)
- Candidate portal (status, feedback, scheduling)
- APIs/embeds for ATS/HCM & calendar systems
- Audit store and monitoring (bias, drift, uptime)
Candidate & recruiter journeys
Candidate journey
- Apply → create profile; consent to video/assessment.
- Record short intro video; receive automated transcription.
- Complete adaptive assessment; optional scenario simulation.
- Track status in portal; receive structured feedback.
- Schedule next steps; provide post‑interview reflections.
Recruiter workflow
- Review ranked list with Composite Fit Scores.
- Drill into transcripts, highlights, and rationale.
- Approve/hold/reject with one click; add notes.
- Bulk move and schedule via integrated calendar.
- Export audit views for stakeholders.
Scoring & explainability
Composite design
The Composite Fit Score fuses technical, behavioral, and experiential evidence with role context. Sub‑scores feed a readable rationale that explains why a candidate is prioritized.
Illustrative rationales
Fit 84: “Strong backend fundamentals; solved adaptive tasks up to level 3; scenario shows calm conflict resolution; communication concise. Minor gap in cloud IAM.”
Fit 69: “Solid growth trajectory; assessment level 2; scenario shows initiative with limited stakeholder mapping; recommend for mentorship track.”
Human oversight & fairness
- Reviewer approvals required before advancing
- Immutable audit of actions and rationale edits
- Continuous bias & drift monitoring across cohorts
Outcomes & KPIs
Organizations typically measure acceleration, recruiter leverage, and quality signals post‑deployment. Establish baselines, then monitor monthly to observe compounding effects as models calibrate.
- Time‑to‑hire
- Recruiter throughput & focus time
- Quality‑of‑hire: early performance, tenure, satisfaction
- Candidate experience: response times, NPS
- Compliance & fairness indicators
Percentages are representative of Intelletto’s AVAR claims; use pilot baselines for accurate targeting.
Trust, compliance & responsible AI
Explainability
Readable rationales and sub‑scores; editors can refine wording with change history captured.
Responsible AI
Monitoring for distributional shift, adverse impact, and lineage with alerting to accountable owners.
Privacy
Consent management, data minimization, retention controls, and candidate transparency via portal.
Adoption plan
- Week 0–1: Connect ATS/HCM, calendar, and assessment catalogs; define roles & KPIs.
- Week 1–3: Configure assessment blueprints; pilot video capture; calibrate Composite Fit Score.
- Week 3–6: Run controlled pilot; gather reviewer feedback; adjust thresholds and rationales.
- Week 6–10: Enable candidate portal feedback; switch on bias/drift alerts; finalize audit exports.
- Week 10+: Scale to more roles; institutionalize dashboards and continuous learning.
Limitations & risks
- Signal quality: Low‑effort video or thin experience reduces predictive value; coach candidates and use minimum quality gates.
- Assessment leakage: Rotating item banks and proctoring reduce gaming risks; monitor anomalies.
- Change management: Recruiter adoption depends on training and explainability; schedule enablement sessions.
- Integration friction: ATS/HCM constraints may require phased embedding or API quota planning.
Appendix: glossary & references
- Composite Fit Score
- Unified score combining technical, behavioral, and experiential sub‑scores with an explainable rationale.
- Behavioral Simulation Interview
- Scenario‑based evaluation capturing decision‑making, leadership, and communication.
- Bias & Drift Monitors
- Systems that track fairness and stability over time and trigger review when thresholds are exceeded.
This document paraphrases publicly available descriptions of Intelletto’s AVAR offering and organizes them as a formal white paper.