AI HR Screening

High-volume hiring breaks when every résumé gets a different subjective skim. AI-assisted screening works when you publish rubrics, measure consistency, and keep recruiters in control of final decisions—not when a model silently drops applicants.

HiMat Technology builds screening workflows as software on Next.js and Node.js: ingest from your ATS or application form, extract structured fields, score against job-specific criteria, and route edge cases to humans with full transcript and score rationale preserved in MongoDB.

We separate environments so test runs never mix with production candidate records. API keys for OpenAI or other leading LLM providers live in your secret stores; PII is minimized in prompts where redaction still allows fair scoring.

Bias-aware design means periodic rubric reviews, override logging, and optional blind stages you define—not claims of algorithmic fairness certifications we cannot substantiate. Recruiters see why a score was suggested and can disagree with audit-friendly notes.

Integrations export approved summaries back to your ATS or spreadsheet workflows. Agent frameworks may orchestrate parsing steps, but approvals remain explicit human actions before candidates advance or receive templated messages.

Engagements start with discovery on volume, roles, compliance constraints, and data residency. We deliver a written scope, staging demo, and rollout plan aligned to IST-friendly standups for global HR stakeholders.