AI recruitment with explainable candidate intelligence

• Opportunity — an employer creates a job or hiring drive and reviews the structured requirements extracted from the job description before publishing.
• Eligibility — the employer's own criteria determine who may apply or be invited; reasons are shown, not hidden.
• Screening — applications are screened against published requirements, producing per-requirement status, a score and a ranking under employer-set thresholds.
• Assessment — configured assessment stages are sent explicitly by a recruiter, scored on the server, and never auto-sent.
• Communication evaluation — structured communication checks add a dimension that résumés cannot show.
• Interviews — panels, availability, candidate slot selection, attendance, rubrics and private individual feedback.
• Decision — recruiters and hiring managers move candidates through a versioned workflow; every transition is recorded.

Airaw does not autonomously hire, reject or rank candidates out of human sight. Screening and assessment produce evidence and explanations; advancing, holding or rejecting a candidate requires an explicit action by an authorised person. Manual moves that contradict a threshold require a recorded reason.

• Every score traces to a specific requirement and the evidence behind it.
• The job description version and competency map used for a screening run are recorded with the result.
• Re-screening against a newer job description preserves earlier results as history.
• Protected characteristics are excluded from requirements and scoring.
• Stage transitions, bulk actions and overrides are written to an audit trail.

Campus drives run through placement cells and direct hiring runs through the employer's own pipeline, but both use the same eligibility, screening, assessment and interview machinery. See Airaw for institutes and Airaw for employers.