Match candidates to jobs by meaning, not keywords.
Keelzo's semantic AI reads what your job requires and what your candidates have done, then matches them across 6 dimensions. No boolean queries. No keyword tuning. Just your ranked shortlist.
What Is Semantic AI Candidate Matching?
Semantic AI candidate matching uses natural language processing (NLP) to understand the meaning of job requirements and candidate experience, not just whether the same words appear in both documents. A candidate who "built payment APIs at scale" matches a "fintech backend engineer" role even without the exact phrase in their resume, because the AI understands the relationship between the concepts.
This is fundamentally different from keyword-based ATS matching, which only scores candidates higher when their resume contains exact words from the job description, a system easily gamed and systematically missed qualified candidates who described their experience differently.
How AI Matching Works in Keelzo
From job posting to ranked shortlist, the exact process:
- Define your job requirements, write your job description naturally. The AI reads it as a human would.
- AI extracts role criteria, skills, seniority level, industry context, experience depth, without you specifying keywords.
- Semantic matching runs, every candidate's profile (inbound and from your talent pool) is evaluated against the criteria using vector embeddings.
- Candidates are scored across 6 dimensions: Skills, Experience, Seniority, Industry, Education, Career Stability.
- Your shortlist appears, ranked by total fit score, with a breakdown of each dimension for every candidate.
Semantic Understanding
Matches by meaning. 'Led infrastructure reliability team' matches 'DevOps engineer' without exact keyword overlap.
Inbound + Rediscovery
Matches run on new applicants AND your existing candidate database simultaneously, surfacing your best past candidates alongside fresh ones.
Transparent Match Reasons
Every match comes with a breakdown, why this candidate, what they scored on each dimension, what to probe in the interview.
No Query Writing
No Boolean strings. No keyword lists. Write a job description naturally, the AI handles the matching logic automatically.
Bias-Reduced
Matching inputs are professional signals only. Name, gender, age, and photo are not factored into match scores.
Sub-Minute Results
Matching runs in seconds on inbound applications. Your past candidate rediscovery runs in under 22 seconds.
When AI Matching Has the Most Impact
Semantic Matching vs Keyword Matching
The practical difference is significant:
- Keyword matching rewards candidates who reverse-engineer your job description. Semantic matching rewards candidates who have done the actual work.
- Keyword matching misses candidates with strong adjacent skills who use different terminology. Semantic matching catches them.
- Keyword matching can't evaluate depth: 10 mentions of 'Python' doesn't mean 10 years of Python. Semantic + 6D scoring evaluates experience depth.
- Keyword matching produces flat lists. Semantic matching produces ranked, explained shortlists.
Keyword matching finds the best optimisers. Semantic matching finds the best candidates.
Frequently Asked Questions
Quick answers about ai candidate matching | semantic job-to-candidate matching.
Match by meaning. Rank by fit. Hire faster.
Keelzo's semantic AI matching gets you from job posting to ranked shortlist in seconds. Free to start.