Screening · Resume parsing
Resume parsing built into the screening flow
Resume parsing is usually treated as a separate plumbing job: extract the fields, dump them somewhere, then figure out scoring later in another tool. That hand-off loses context and means the parsed data rarely turns into an actual screening decision without more manual work.
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 builds resume parsing directly into the screening flow. Each resume is parsed into structured data the moment it arrives, and that same data immediately feeds your knockout criteria, screening questions, role-fit scoring, and ranking. There is no second tool and no lost context. Recruiters get a ranked pile with transparent reasons, a human makes every decision, nothing is auto-rejected, and the 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 parsing
Parsing feeds scoring
Parsed fields flow straight into role-fit scoring and ranking, so extraction is not a dead end but the first step of a complete screen.
One connected flow
There is no separate parsing tool to integrate, so context from the resume stays attached to every screening decision and reason.
Evidence stays linked
Each score and ranking links back to the parsed resume data behind it, so recruiters can verify exactly what the screen read.
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 each resume into structured data on arrival
- Feeds parsed data straight into knockout and role-fit scoring
- Keeps resume context attached to every screening decision
- Ranks the pile with transparent, evidence-linked reasons
- 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 to support EEOC standards 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 parsing
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Learn moreStop reading the inbound pile by hand. Put screening on autopilot.
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