Blog
Notes on resume screening, explainable AI hiring, and building a shortlist you can actually defend.
5 Signs Your Resume Screening Process Is Losing Good Candidates
Keyword filters and gut-feel skims both quietly drop qualified people — often without anyone noticing. Here's what that looks like in practice.
Why a 92% AI Match Score Can Be Meaningless Without Context
A single percentage tells you nothing about why a candidate scored that way — and that's exactly the problem with most AI screening tools.
Resume Screening Beyond Tech: Hiring for Skilled Trades, Healthcare, and Hospitality
Most AI screening tools are tuned for software engineering resumes. Here's why that approach breaks down for the majority of hiring.
How Does AI Resume Screening Actually Work? A Plain-English Breakdown
No jargon, no marketing language — here's what actually happens between uploading a resume and getting a ranked shortlist.
Free Resume Screening Software: What "Free" Actually Means
Free tools usually make money somewhere — here's how to spot the tradeoffs before you commit candidate data to one.
How to Screen 100 Resumes Quickly Without Losing the Human Review
A large applicant pool does not require a rushed shortlist. Use the job description as the source of truth, rank candidates, and keep the evidence visible.
The Resume Screening Checklist Recruiters Can Reuse for Every Role
Use the same simple checklist for every req so screening stays consistent when the applicant pool grows.
Semantic Resume Search vs. Keyword Matching: What Recruiters Should Know
Exact keywords are useful for some hard requirements, but they are a poor substitute for understanding what a candidate actually did.
How Small Recruiting Teams Can Use AI Screening Without Losing Control
Small teams can save screening time without handing hiring decisions to an opaque model. Start with clear criteria and keep the reasoning visible.
Resume Screening Scorecard Template for Recruiters
A simple scorecard helps recruiters compare candidates consistently without reducing the hiring decision to one unexplained number.
What Is Explainable AI in Hiring? A Recruiter's Guide
Explainable AI in hiring means every match comes with a reason you can check — here's what that looks like in practice, not just as a buzzword.
ATS vs. AI Resume Screening: What's Actually Different
An ATS and an AI resume screener solve different problems — conflating them is why a lot of hiring software ends up disappointing people.
How to Write a Job Description That Attracts Qualified Candidates
A vague job description produces a vague applicant pool. Here's what actually changes the quality of who applies.
The Hidden Cost of Manual Resume Screening
Manual resume screening doesn't show up as a line item on a budget, but the hours — and the missed candidates — add up fast.
Skills-Based Hiring: What It Means and How to Actually Do It
Skills-based hiring means screening for what a candidate can actually do — here's what that looks like in a real process, not a mission statement.
How to Reduce Bias in Resume Screening
Reducing bias in resume screening is about process design, not good intentions — here are the concrete changes that actually matter.
Time-to-Hire: What It Actually Measures and How to Improve It
Time-to-hire is one of the most quoted recruiting metrics and one of the most misused — here's what it actually measures.
How Resume Parsing Works (And Why It Breaks on Some Resumes)
Resume parsing turns a document into text a computer can reason about — and it's also where a lot of screening quality quietly breaks down.