AI Semantic Matching Find 40% More Candidates.
Stop losing qualified candidates to keyword search limitations. Yena's AI understands context, job title variations, and skill relationships—finding 25-40% more candidates your competitors miss.
The Fatal Flaw of Keyword-Only Search.
Traditional ATS uses keyword matching: Search 'Senior Python Developer' finds exact matches only. Misses 'Backend Engineer' (Python-heavy), 'Data Engineer' (Python pipelines), 'ML Engineer' (TensorFlow/PyTorch).
Finds: 23 exact matches
- Misses: 'Backend Engineer' (Python stack)
- Misses: 'Data Engineer' (Python pipelines)
- Misses: 'ML Engineer' (TensorFlow = Python)
Result: 23 candidates, 40% missed
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.
Powered by the same class of AI technology behind the world's best search engines.
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."