AI resume screening
AI resume screening with the evidence attached.
Every applicant scored against the role's real requirements, with the line that earned each score shown next to it. You read a shortlist and the reasoning, not a number.
What you get back
A score you can argue with.
Requirements, scored one at a time
Must-haves and nice-to-haves are assessed separately and shown met or unmet, so a candidate who is strong everywhere except the one thing that matters does not disappear into a good-looking average.
Skill depth, not skill presence
Each skill gets a level rather than a tick. Eight years of shipping it and one line in a skills list are not the same claim, and a keyword filter cannot tell them apart.
Seniority read from the work
Inferred from what the person actually did rather than from the title they were given, which varies more between companies than between people.
What is missing, named
The requirements and terms from your description that do not appear anywhere in the application — the fastest way to see whether a gap is real or just unstated.
Questions to ask this person
Generated from the specific gaps in this specific application, so the first interview starts where the CV stopped being informative.
How it stays defensible
Ranking, not rejecting.
No automatic rejections
The system orders your reading; it never removes anyone. Every rejection in your pipeline was made by a person who can say why.
Evidence under every number
Open any score and land on the passage it came from. A score with no traceable source is a guess wearing a decimal point.
Blind screening
Names, photos, schools and dates can be withheld from the scoring pass, so the model is working from experience rather than from proxies.
Exportable for audit
Every score, its evidence and the rubric it was measured against export together — which is what an audit actually needs.
The same rubric for everyone
A run applies one rubric across the whole pool in one pass. Consistency is the part human review genuinely cannot do at volume.
You wrote the rubric
Requirements and weights come from your job description and your judgement. The model applies them; it does not decide what matters.
Questions about AI resume screening
- What is AI resume screening?
- Scoring each application against the requirements of a specific role, rather than filtering on keyword matches. The useful version tells you why it scored what it did — which requirements were met, which were not, and the line in the CV it relied on — so the score is something you can check rather than something you have to trust.
- Does the AI reject candidates automatically?
- No. It ranks and explains; it never rejects. Nobody is removed from your pipeline without a person deciding, and the ranking is a reading order rather than a verdict. This is deliberate, and it is also what keeps you on the right side of hiring regulation in most jurisdictions.
- How do you handle bias?
- Names, photos, schools and graduation dates can be hidden from the scoring pass, so the score is formed from experience and evidence rather than proxies for background. Every score is exportable with the evidence attached, which is what makes an audit possible at all. No screening system is bias-free, including this one; what we can offer is that it is inspectable.
- What does it actually check?
- The must-haves and nice-to-haves you set for the role, scored individually; per-skill depth rather than presence or absence; seniority inferred from the work rather than the job title; and the keywords from the description that are present and missing. Strengths, gaps and suggested interview questions come out of the same pass.
- Can it screen a whole applicant pool at once?
- Yes. Point a run at a job and every applicant is scored against the same rubric in one pass, which is the point — consistency across the pool is the thing a human reviewer cannot hold on to after the fortieth CV.
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Read the shortlist, not the pile.
Score a real applicant pool against a real job description and see whether the ranking matches your instinct.
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