Rank a folder of resumes against your job spec.
This use case works best with the following environment variables, which are injected into your sandbox at runtime:
OPENAI_API_KEYA hundred resumes for one role is a reading problem, not a judgment problem, the judgment takes minutes once the reading is organized. Most AI screening tools get this backwards: they hide the reading and the judgment behind a score you can't interrogate. This blueprint takes the opposite stance: organize the evidence, cite everything, and leave the decision where it belongs, with you.
A screening pipeline with a job spec editor (role, must-haves, nice-to-haves), bulk resume upload, and AI scoring where every score must cite the exact resume lines that justify it. The ranked review board shows the breakdown per criterion, the cited evidence, and a red-flag column for gaps and missing must-haves. Shortlist export includes the reasoning. Nothing auto-rejects; all AI output is framed as advisory.
It ships with six realistic sample resumes of varying fit, so you can watch how the scoring behaves, including where it hedges, before uploading real candidates.
Is this compliant to use for hiring? The tool is deliberately advisory-only, it organizes evidence and never auto-rejects, which is the posture responsible-AI hiring guidance points toward. Your hiring process and jurisdiction's rules still apply; the audit trail of cited evidence helps you meet them.
What does it cost to run? The dashboard shows cost per screening batch. Screening uses your own OpenAI API key, typically cents per batch, not per-seat SaaS pricing.
Can candidates' data stay private? Resumes are processed in your sandbox with your API key. No third-party recruiting platform sees them.