Your screening layer is filtering out real candidates, not just bad ones
If any part of your pipeline — an ATS's built-in parser, a semantic-matching layer, an LLM doing a first-pass summary — processes a CV before a human does, then a meaningfully large share of what looks like "this candidate doesn't fit" is actually "this candidate's CV didn't survive being read." Those are different problems, and only one of them is about the candidate's actual qualifications.
What breaks a good candidate's chances before you ever see them
Multi-column layouts that scramble reading order. Text rendered as an image, invisible to any parser. Non-standard section headers a parser doesn't recognize as "Experience." Sparse entries — a title and two dates with nothing underneath — that give a matching system nothing to score, even when the underlying role was substantial. None of this correlates with candidate quality. It correlates with which CV template the candidate happened to pick.
What "AI-ready" means from your side of the pipeline
A candidate whose CV is AI-ready isn't gaming your system. They're just not accidentally sabotaging themselves against it. Real, extractable text. Standard structure. Achievements described with enough specificity that your semantic-matching layer, if you're running one, has something real to compare against the posting instead of a vocabulary list. The candidates this actually filters out are the ones padding with keywords they can't back up — exactly the filtering you want your pipeline doing.
A tool for candidates that's also useful to you
Our free AI-Readiness Check scores a CV on structure, extractability, content depth, and coherence, and flags exactly what a screening system would struggle with — roughly the same categories your own pipeline is implicitly scoring against. A candidate who runs their CV through it before applying is fixing the same failure modes your ATS would otherwise silently penalize. It costs them nothing and costs you nothing. It just means more of what reaches your desk is candidates your system could actually read correctly, not a sample skewed by who happened to format well.
What's coming
A dedicated recruiter-side view — sending a candidate a posting directly, seeing their Fit Check results, tracking responses — is in active development, not live yet. Today, the candidate-side tool already works standalone: anyone can run a Fit Check against a posting you've made public, with nothing required on your end.
Related guides
- How CV screening actually works in 2026 — the full pipeline this guide assumes
- What "AI-ready" actually means — what the check is actually scoring