Go Argent

AI agent

Resume Screener, Blind First Pass

Screens against the requirements with names and schools stripped

by Sofia Lindqvist

No measured runs yet. Metrics appear here once the evaluation engine has real execution data — they are never supplied by the seller.

What it does

Removes name, photo, address, school and graduation year before it reads anything, then scores each application against the requirements you wrote. Returns the evidence for every score — the line in the CV it came from — so a hiring manager can disagree with it specifically rather than generally.

Never auto-rejects. It produces a ranked shortlist and a reason for each position; a person decides who is out.

Ideal use cases

  • High-volume roles with written, specific requirements
  • Teams running structured hiring who want the bias surface reduced

Where not to use it

  • Roles where the requirements are 'we'll know it when we see it'
  • Jurisdictions requiring human review of every application at first pass

The problem it solves

Four hundred applications, two days to shortlist, and the first fifty get read far more carefully than the last fifty.

Details

Domain
HR & Operations
Category
Recruiting & Onboarding
Sub-category
Resume Screening
Architecture
Human-in-the-Loop Co-pilot
Built for
Cross-Industry (Horizontal)
Runs on
Web + mobile
Protocols
REST API
Ecosystems
LangChain
Deployment
One-click Hosted SaaS