Introduction to Semantic Search
- What is semantic search
- How it differs from a keyword or Boolean search
- Why modern AI sourcing relies on context, meaning, and relationships between skills, experiences, and roles
- The evolution from keyword-matching to contextual understanding
How Semantic Search Works
- The AI analyzes candidate data, job descriptions, and contextual signals using large language models (LLMs)
- Concepts like vector embeddings and similarity scoring
- How the system understands synonyms, related skills, and domain context (e.g., “RN” ≈ “Registered Nurse”)
- Continuous learning — improving as more searches and hires occur
Semantic Search vs. Boolean Search: A Comparison
| Feature | Boolean / Keyword Search | Semantic (Smart) Search |
|---|---|---|
| Logic | Matches exact words or Boolean operators | Understands context, meaning, and intent |
| Skill Recognition | Requires manual inclusion of synonyms | Automatically expands related skills |
| Example | “(nurse OR RN) AND ‘emergency room’” | “Emergency room nurse” → finds all relevant profiles |
| Maintenance | High manual effort | Adaptive and self-improving |
| Accuracy | Often broad or irrelevant | High-precision matching based on contextual fit |
Case Study: LinkedIn Recruiter vs. Asendia AI Semantic Search
Example scenario:- Boolean search in LinkedIn Recruiter:
("registered nurse" OR "RN") AND ("emergency room" OR "ER") AND ("Arizona")
→ Returns candidates with matching keywords, but misses profiles with similar experiences written differently. - Asendia AI Semantic Search:
Input: “Experienced ER nurse with trauma background in Arizona”
→ Returns candidates with matching experience descriptions even if they never used “ER” or “trauma” explicitly — e.g. “Level 1 hospital nurse,” “critical care nurse,” etc.
Asendia AI delivers contextually relevant matches, not just keyword hits.
How Asendia AI Finds the Best-Fit Candidates
- Combines semantic understanding, skill clustering, and experience mapping
- Evaluates role compatibility using deep embeddings trained on millions of job–candidate pairs
- Factors in:
- Skill relevance
- Seniority level
- Industry experience
- Location and availability
- Produces a fit score and contextual summary per candidate
Continuous Learning and Feedback Loop
- Each recruiter interaction (e.g. shortlist, reject, hire) trains the system
- AI learns recruiter preferences, improving future sourcing accuracy
- Adaptive intelligence: the sourcing agent becomes more personalized for each client
Integration with Asendia AI Platform
- Works natively with Tracker, Bullhorn, or internal ATS systems
- Pulls live job data and searches candidate pools automatically
- Outputs top matches, ranking scores, and summary insights directly into the ATS
Benefits for Recruiters
- Dramatically reduces sourcing time
- Uncovers hidden matches not found by keyword search
- Improves submission-to-placement ratio
- Enables personalized, data-driven outreach
