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ResDB Talent Pool & Semantic Sourcing

ResDB is the neural candidate database powering XeCubes. Unlike legacy ATS databases that rely on rigid keyword matches, ResDB indexes every resume, past interview transcript, and project repository into high-dimensional vector space using fine-tuned embedding models.

How Semantic Search Works​

When a recruiter enters a natural language query or imports a job description, ResDB computes cosine similarity between the query embedding and candidate vectors:

flowchart TD
    JobDesc[Job Description or Natural Language Query] --> Embedder[XeCubes Neural Embedding Engine]
    Embedder --> Vector[1536-Dimensional Query Vector]
    Vector --> PgVector[(PostgreSQL + PgVector HNSW Index)]
    PgVector --> TopK[Top-K Nearest Neighbor Candidate Profiles]
    TopK --> ReRanker[LLM Re-Ranking & Calibration]
    ReRanker --> RankedMatches[Ranked Candidates with Explanation Badges]
  • Synonym & Domain Mastery: Recognizes that "Distributed systems with Paxos/Raft" implies strong Go/Rust or C++ background even if specific words are missing.
  • Talent Rediscovery: Unlocks dormant candidates from past recruitment campaigns who previously reached final rounds.
  • Zero Keyword Stuffing Sensitivity: Context-aware embeddings evaluate depth of contribution rather than keyword repetition count.

Using Semantic Search in the Dashboard​

  1. Navigate to Talent Pool → Search from the recruiter dashboard.
  2. Enter a natural language query in the search bar: 
    Senior Site Reliability Engineer with high-traffic Kubernetes and eBPF tracing experience
  3. Apply optional structured filters:
{
  "semantic_query": "Senior Site Reliability Engineer with high-traffic Kubernetes and eBPF tracing experience",
  "filters": {
    "locations": ["San Francisco, CA", "London, UK", "Remote"],
    "min_experience_years": 5,
    "max_notice_period_days": 30,
    "skills_required": ["Kubernetes", "Linux Kernel", "Go"],
    "salary_expectation_max": 220000
  },
  "top_k": 25
}
  1. Results appear ranked by semantic relevance with AI match scores and explainability badges.

Candidate Explainability​

Every candidate returned by ResDB includes an AI Match Scorecard:

ComponentDescription
Core Competencies MatchDirect alignment with required technical stack.
Growth TrajectoryProgression of seniority and scope of ownership in past companies.
Identified GapsMissing certifications or technologies requiring ramp-up time.
Suggested Interview QuestionsDynamic questions generated specifically to validate candidate strengths or probe gaps.

Programmatic Access​

ResDB is also accessible via the Developer API. See the Resume Parser API for uploading candidate data programmatically.