Screening · Resume parser
Resume parser that scores and ranks, not just extracts
A plain resume parser turns a PDF into structured fields: name, titles, dates, skills. Useful, but parsing alone still leaves a recruiter staring at hundreds of tidy records with no idea which ones to read first. Extraction is the start of screening, not the end of it.
Parse · score · rank · recruiter reviews
Held for recruiter review · never auto-rejected
Screen the pile to watch Prescreener parse every resume, apply your criteria and knockouts, and rank the inbound applicants with a transparent reason on each.
Live, interactive · AI assists, you decide · no signup needed
AI assists · you decide · bias-audited (EEOC / NYC Local Law 144)
Human-in-the-loop you decide
EEOC and LL144 bias-audited
Prescreener parses every inbound resume into clean structured data, then goes the distance most parsers stop short of. It applies your knockout and eligibility criteria, asks screening questions, scores role-fit, and ranks the pile with a transparent reason behind each placement. Recruiters review the top matches first, a human makes every decision, and nothing is auto-rejected. The whole flow is bias-audited for EEOC and NYC Local Law 144, with candidate consent and AI disclosure.
Why it works
What your team gets with resume parser
Clean structured data
Every resume becomes consistent fields you can filter and search, even from messy PDFs and unusual formats, so the data is reliable before any scoring begins.
Parsing plus scoring
Where most parsers stop at extraction, Prescreener scores role-fit and ranks the pile, so structured data turns directly into a prioritized review list.
Transparent placement
Each candidate ranking links back to the criteria and the parsed evidence behind it, so recruiters see exactly why someone landed where they did.
What it handles
Parsed, scored and ranked on autopilot
Prescreener reads every inbound resume, applies your knockout and eligibility criteria, asks the screening questions you set, scores role-fit against consistent criteria, and ranks the applicant pile so your recruiters review the top matches first.
- Parses every resume into clean, consistent structured data
- Handles messy PDFs and unconventional resume formats
- Scores role-fit and ranks the pile beyond simple extraction
- Shows the parsed evidence behind every ranking
- Keeps consent, AI disclosure, and EEOC and NYC Local Law 144 auditing in place
Why Prescreener
One step that screens the whole inbound pile
Not a full ATS, not a six-figure assessment suite, and not a staffing agency. Parse, score, rank and hand off in one place, shaped to the criteria you already hire on.
Reads every resume
Prescreener parses every inbound application, applies your knockout and eligibility criteria, and asks the screening questions you set. Applicants consent and are told AI is screening their application, so no qualified person sits in a backlog for days.
Scores role-fit consistently
Every applicant is scored against the same criteria, with a transparent reason on each candidate, so the pile is compared consistently and the scoring stays bias-audited against EEOC guidance and NYC Local Law 144.
Ranks the pile
The strongest matches rise to the top of a ranked pile your recruiters review first. Prescreener never auto-rejects, it ranks and flags candidates for review, and your team makes every decision.
Good questions
Questions about resume parser
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Set your role and criteria and Prescreener parses every inbound resume, scores role-fit, and ranks the pile for your team. Prescreener ranks and flags candidates, your recruiters make every hiring decision.
Consistent criteria · bias-audited to EEOC and LL144 · human-in-the-loop