Understanding Semantic Search - Talent smart search

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

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

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

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.

Result:
Asendia AI delivers contextually relevant matches, not just keyword hits.

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

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