3× faster shortlist
Talent graph: 3× faster consultant discovery in closed networks
Chief Technology Officer•Noble House Consulting•Jan 2024 — Jun 2024•Published 14 March 2024
Replaced linear talent-pool search with a graph-based discoverability model for closed consultant ecosystems — context, skills, and relationship-aware matching.
Technology Stack
Neo4jPythonReactPostgreSQLGraphQL
Key Outcomes
- •3× faster time-to-shortlist for niche consultant requests
- •Graph queries outperforming legacy SQL search on complex skill combos
- •Published as peer-reviewed Zenodo paper on talent graphs
Outcome: 3× faster shortlist for complex consultant searches.
Context
Noble House operates a closed consultant network. Traditional “talent pool” search broke down when clients needed rare skill combinations and relationship context.
Problem
- SQL
LIKEqueries missed adjacency (who worked with whom, on what). - Recruiters spent hours manually cross-referencing spreadsheets.
- Privacy constraints prevented open marketplace models.
Approach
- Model consultants, skills, engagements, and referrals as a property graph.
- GraphQL API for recruiter UI; hybrid search (vector + graph traversal).
- Privacy rules encoded at query layer — no raw PII in graph exports.
Architecture
PostgreSQL (source of truth) → sync jobs → Neo4j → GraphQL → React recruiter UI.
Research
Full framework published: From Talent Pools to Talent Graphs.
Results
Median time-to-shortlist dropped by two-thirds on multi-skill requests; recruiter satisfaction scores rose in Q2 2024 pilot.
CTA
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