Overview
Semantic search analyzes meaning behind words to uncover the best-fit talent faster — understanding skills, experience, and context beyond simple keyword matches.Data Foundation
Asendia AI’s sourcing engine is powered by a global database of over 200 million professional profiles, aggregated from public and compliant data sources.Through partnerships with trusted data providers, Asendia AI compiles candidate information from verified sources such as LinkedIn, public resumes, and professional networks, enriched with available contact details (phone number and email). This comprehensive dataset enables the AI to perform deep semantic analysis across massive talent pools, ensuring each search delivers accurate, up-to-date, and actionable results.
1. Natural Language Input
Recruiters can describe the role naturally, just as they would explain it to a colleague.For example:
“Find senior full-stack engineers experienced with React, Node.js, and cloud deployment.”The AI interprets this intent, extracts the relevant entities, such as role, skills, experience level, and technologies, and transforms them into structured filters.
No Boolean syntax is required; the system understands the recruiter’s intent behind the words.
Extracted Filters
After the recruiter submits the search, the AI displays a Filters Tab that visualizes all extracted criteria, such as:- Role: Full-Stack Engineer
- Tech Stack: React, Node.js, AWS
- Seniority: Senior
- Location: Remote or specific geography
- Experience: 5+ years
Filters Page Explained
The Filters Page gives full visibility and control. It’s organized into sections that matter most for engineering searches:- Core Role Filters: Job title, specialization (e.g., Backend, DevOps, Frontend)
- Technical Skills: Languages, frameworks, cloud platforms, and tools
- Experience Filters: Years of experience, seniority, or domain (e.g., fintech, healthtech)
- Location & Work Type: On-site, hybrid, remote, or region-specific
4. AI Matching Process
The sourcing engine uses a two-step ranking system to ensure precision in technical talent matching:- Ranking: Scores candidates based on direct keyword and metadata relevance (e.g., exact skills, titles, and experience).
- Re-ranking: Applies deep semantic understanding to analyze project descriptions, role context, and related technologies — identifying candidates who may use different terminology but have equivalent experience (e.g., “TypeScript” ≈ “JavaScript,” “GCP” ≈ “AWS”).
5. Search Results
The results show a ranked list of engineering candidates, each with:- Fit Score: AI-calculated probability of match
- Top Skills & Technologies: Automatically highlighted from their experience
- Contextual Summary: Why they fit the specific role based on projects, frameworks, and industry background
