Resume Screening and Shortlisting
A single open role can pull hundreds of applications, and most teams still rely on keyword filters or a recruiter skimming resumes for ten seconds each. Keyword filters reject strong candidates who described their experience differently than the job posting did, while a rushed manual skim lets weaker applications through simply because they used the right buzzwords. Hiring managers end up interviewing candidates who look good on paper but don't match the role, while genuinely qualified people never make it past the first pass because their resume was formatted in a way the system couldn't parse.
STARTING PRICE
From €299
Standard tier · Multi-step workflow with AI extraction/decisioning and 2-3 integrations.
Get a quote →Saves roughly 8-12 hrs/week for a recruiter handling multiple open reqs.
How the automation works
We build a screening layer that reads resumes for actual experience and competency rather than exact keyword matches, mapping non-standard formats, career changers and varied job titles back to the requirements that matter for the role. Each candidate gets ranked with a visible reasoning summary — which requirements are met, which are partial, which are missing — so a recruiter can override the ranking with full context instead of trusting a black-box score. Candidates near the cutoff are flagged for human review rather than auto-rejected, and the shortlist that reaches the hiring manager comes with the reasoning attached, cutting review time without removing judgment from the decision.
Process flow
- 01
Application received trigger
A new application lands in the ATS and enters the screening queue automatically, no manual export or batch review needed.
- 02
Parse resume content ai
Resume text is extracted and structured, handling non-standard formats, career-change narratives and varied job titles rather than relying on rigid keyword matching.
- 03
Rank against job requirements ai
Each candidate is scored against the role's actual must-have and nice-to-have requirements, with a visible breakdown of which are met, partial or missing.
- 04
Flag borderline candidates ai
Candidates near the ranking cutoff are routed for human review rather than auto-rejected, since the highest-risk errors happen right at the boundary.
- 05
Generate reasoned shortlist output
The recruiter receives a ranked shortlist with the scoring rationale attached, so the decision to advance or pass a candidate is informed, not a black box.
- 06
Sync status to ATS integration
Screening outcomes and rationale are written back into the ATS candidate record, keeping the pipeline current for hiring managers checking status.
Inputs
- Job requisition with must-have and nice-to-have requirements
- Incoming resumes/applications from ATS
- Historical hiring outcomes for the role (optional calibration)
Outputs
- Ranked candidate shortlist with reasoning
- Borderline-candidate review queue
- Requirement-match breakdown per candidate
- Time-to-shortlist metric
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- Resume parsing misses non-standard formats — resumes built in design templates, PDFs exported from portfolio tools, or resumes structured around a portfolio link rather than a chronological work history all break naive text extraction, and candidates get silently mis-scored rather than flagged as unparseable.
- Over-filtering on exact keyword match excludes strong candidates who describe the same experience differently — someone who led 'customer implementations' has the skills a posting asking for 'client onboarding' wants, and a literal match filter drops them before a human ever sees the resume.
- Automated rejection emails sent immediately on a low score close off candidates who are actually still viable for a different open role — rejections should route through a human checkpoint, not fire automatically the moment a score computes.
- A ranking model trained on past 'successful hire' data can quietly encode whatever bias shaped historical hiring at the company — if past hires skewed toward a particular school or employer, the model will over-weight those signals unless the requirement set is defined explicitly rather than inferred from resume similarity to prior hires.
Frequently asked questions
Does this replace the recruiter's judgment?
No — every candidate near the cutoff routes to a human, and the ranked shortlist includes the reasoning behind each score so the recruiter can override it with full context rather than accepting a bare number.
How does it handle resumes that don't follow a standard chronological format?
Parsing is built to map varied formats and non-linear career histories back to the role's requirements; anything the parser can't confidently structure gets flagged for manual review instead of silently mis-scored.
Can we adjust which requirements matter most for a specific role?
Yes, the must-have and nice-to-have weighting is set per requisition, so a senior technical role and a customer-facing role don't get screened against the same generic criteria.
Will strong candidates get auto-rejected without a human looking at their application?
No — auto-rejection is deliberately excluded from this workflow; low-scoring and borderline candidates are queued for human review rather than rejected automatically.