Stop losing great candidates to rigid keyword filters. GoVerse matches skills semantically, understanding equivalences, transferable experience, and adjacent competencies that keyword systems miss.
AI reads the full CV and extracts skills, tools, methodologies, and competencies, including those implied by experience descriptions, not just listed in a skills section.
Each skill is matched against your requirements using vector embeddings, understanding synonyms, abbreviations, equivalencies, and skill hierarchies (e.g., "AWS" includes "EC2, S3, Lambda").
Skills are weighted by importance (must-have vs nice-to-have), recency, and depth of experience. A candidate who used React for 3 years in production scores differently from one who listed it in a course.
Every score comes with a breakdown: which skills matched, how strongly, what's missing, and what's a bonus. Recruiters see exactly why: no black box.
No black-box AI. Recruiters and hiring managers see exactly why each candidate scored the way they did.
AI reasoning: "Strong match on all required skills with production depth. Leadership experience exceeds requirement. Minor gap on GraphQL is mitigated by strong API design experience."
Understands programming language ecosystems, framework versions, cloud provider equivalencies, and methodology variations.
Maps clinical competencies, certification equivalencies across jurisdictions, and specialist training that doesn't use standard terminology.
Surfaces candidates with transferable skills from adjacent industries who keyword filters would never find.
Rediscover existing employees with skills for new roles. Match against internal openings based on demonstrated competencies.
Score hundreds of applicants in minutes with consistent, explainable criteria. No reviewer fatigue or inconsistency.
Search your entire candidate database semantically. Find people who applied years ago but are perfect for today's role.
Two candidates both list React. One shipped a production app to 100,000 users over three years and mentored a team through it. The other completed a weekend course and put it under "skills". A keyword filter treats them as identical, because the word is present in both. That is the flaw that sends good hiring managers back to reading CVs by hand. GoVerse weights each matched skill by depth, recency and how it was actually used, so the first candidate ranks well above the second even though the keyword match is the same. The score reflects competence, not vocabulary.
Weighting also lets you say what matters for this role rather than accepting a flat checklist. Mark a skill must-have and a gap there pulls the score down hard. Mark it nice-to-have and a strong candidate is not knocked out for missing it. In the Sarah Chen example above, a GraphQL gap barely moved her 87 because it was flagged nice-to-have and her API design experience was judged transferable. That single decision (which skills are dealbreakers and which are bonuses) is where most bad shortlists are made, and it is the decision GoVerse makes visible instead of burying it in a filter. The same weighting drives every AI candidate screening run.
Skills matching is strongest at the top of a high-volume funnel. A role with 200 applicants gets scored consistently in minutes at roughly $0.01 per CV, with no reviewer fatigue and no candidate ranked lower simply because they were read at 5pm on a Friday. It is also where internal mobility quietly wins: the same semantic engine behind semantic talent search can score your existing employees against a new opening and surface people who already have the competencies but the wrong job title. Those matches are almost invisible to keyword search.
What it does not do is decide who to hire. A 92 and an 88 are close enough that the interview, not the score, should separate them. Skills matching tells you who is worth a conversation and why, with the reasoning on the record so a hiring manager or an auditor can see it. The final call, and everything a CV cannot capture about a person, stays with the recruiter. Used that way it removes the grind of the first pass without pretending to replace the human parts of the decision.
No. Every score comes with a breakdown of which skills matched, how strongly, what is missing and what counts as a bonus, plus written reasoning. Semantic matching improves what gets found; explainability makes sure you still see the working behind the number.
Yes. Because matching runs on meaning rather than exact text, it maps adjacent titles and transferable competencies, which is how career changers and internal candidates surface instead of being filtered out for using different words.
Screening runs at roughly $0.01 per CV, so scoring a few hundred applicants costs a few dollars. Per-seat plans start at $49.99, $99.99 and $149.99 with 100, 300 and 600 credits, and the free trial runs a month with no credit card required.
See how semantic skills matching finds candidates your ATS misses. Free for 1 month.
Start free trial →