AI Semantic Matching

Encontre o Candidato Certo, Não Apenas a Palavra-Chave Certa Find 40% More Candidates.

A pesquisa por palavras-chave erra os melhores candidatos. A correspondência semântica com IA entende o significado — encontrando "Diretor de Receita" quando pesquisa "VP de Vendas".

See AI in Action Start Free Trial

Advanced AI25-40% More Candidates10,000+ Job Titles

Senior Python Developer

Python Developer

Exact match

98%match

Backend Engineer

Python stack

91%match

ML Engineer

TensorFlow / Python

87%match

25-40%

more qualified candidates found

10,000+

job title variations understood

3-5 hrs

saved per search vs manual screening

O Problema com a Pesquisa por Palavras-Chave Keyword-Only Search.

Os sistemas ATS tradicionais perdem candidatos qualificados porque não entendem o significado — apenas as palavras.

❌ Keyword-Only ATS

Search: 'Senior Python Developer'

Finds: 23 exact matches

Misses: 'Backend Engineer' (Python stack)
Misses: 'Data Engineer' (Python pipelines)
Misses: 'ML Engineer' (TensorFlow = Python)

Result: 23 candidates, 40% missed

✅ AI Semantic Matching

Search: 'Senior Python Developer'

Finds: All 23 exact matches

+ 'Backend Engineer' (Python-heavy)
+ 'Data Engineer' (Python data work)
+ 'ML Engineer' (Python ML frameworks)

Result: 38 candidates, +65% more

How AI Semantic Matching Works.

Job Title Variations

Understands: 'Staff Engineer' = 'Principal Engineer' = 'Distinguished Engineer' across Google, Amazon, Microsoft, Meta leveling systems.

Search 'Senior Engineer' finds Staff/Principal

Skill Context & Tech Stacks

'Python' includes Django (web), Pandas (data), TensorFlow (ML), Ansible (DevOps)—different contexts for different roles.

Search 'Backend Python' prioritizes Django/Flask

Industry-Specific Expertise

'Primary Care Physician' ≠ 'Hospitalist' (both Internal Medicine, different practice settings).

Search 'Primary Care' excludes hospitalists

Experience Level Nuance

'$150M P&L Plant Manager' requires different experience than '$50M P&L Plant Manager'.

Adjusts for P&L scope, not just title

Practice Specialization

'M&A Tax Attorney' ≠ 'Estate Planning Tax Attorney' (both tax, different specializations).

Filters by transaction type, industries

Continuous Learning

AI model improves with every search. New job titles, emerging skills, industry trends.

New titles like 'GenAI Engineer' incorporated

Real-World Examples.

Healthcare: 'Primary Care Physician'

Keyword ATS problem:

Returns all 'Internal Medicine'—includes hospitalists, ICU, subspecialists

Yena AI semantic:

Finds: Family Medicine, Internal Medicine—Outpatient
Excludes: Hospitalists (inpatient), ICU, subspecialties

32 Primary Care matches vs. 67 generic (52% more precise)

Legal: 'M&A Tax Attorney'

Keyword ATS problem:

Returns all 'Tax Attorney'—includes Estate Planning, SALT, Tax Controversy

Yena AI semantic:

Finds: Tax attorneys with M&A transaction experience
Project signals: '$500M acquisition structuring'

18 M&A Tax specialists vs. 45 generic (60% noise reduction)

Manufacturing: 'Plant Manager with Industry 4.0'

Keyword ATS problem:

Returns anyone mentioning buzzword (most haven't led digital projects)

Yena AI semantic:

Signals: 'Led $5M smart factory,' '40% downtime reduction'
Technology: IoT sensors, MES, digital twin

12 proven leaders vs. 40 mentions (70% precision improvement)

"AI semantic matching changed everything. Before: searching 'Python Developer' found 20-25 exact matches, screening took 4 hours. Now: Yena finds 35-40 candidates including Backend, Data Engineers—all Python-heavy. Screening cut to 45 minutes. We're placing 2x more developers."

Marcus Hoffman, Founder

DevTalent Recruiters (Berlin)

📊 2x developer placements • 4 hours → 45 min screening

Real-World Examples.

Healthcare: 'Primary Care Physician'

Keyword ATS problem:

Returns all 'Internal Medicine'—includes hospitalists, ICU, subspecialists

Yena AI semantic:

Finds: Family Medicine, Internal Medicine—Outpatient
Excludes: Hospitalists (inpatient), ICU, subspecialties

32 Primary Care matches vs. 67 generic (52% more precise)

Legal: 'M&A Tax Attorney'

Keyword ATS problem:

Returns all 'Tax Attorney'—includes Estate Planning, SALT, Tax Controversy

Yena AI semantic:

Finds: Tax attorneys with M&A transaction experience
Project signals: '$500M acquisition structuring'

18 M&A Tax specialists vs. 45 generic (60% noise reduction)

Manufacturing: 'Plant Manager with Industry 4.0'

Keyword ATS problem:

Returns anyone mentioning buzzword (most haven't led digital projects)

Yena AI semantic:

Signals: 'Led $5M smart factory,' '40% downtime reduction'
Technology: IoT sensors, MES, digital twin

12 proven leaders vs. 40 mentions (70% precision improvement)