SkillHub started as a frustration with résumé scraping. Great candidates were reduced to keyword density, and hiring teams couldn’t see how proof mapped to outcomes.
We built a semantic layer that links achievements, artifacts, and endorsements to competency frameworks, empowering reviewers to interrogate depth over buzzwords.
Design the ontology collaboratively
Designers, recruiters, and data scientists co-created the ontology. This mix ensured the model spoke human while remaining machine-actionable.
- Nodes represented competencies, behaviors, and proof artifacts
- Embeddings grouped similar experiences while surfacing anomalies
- Review flows visualized relationship strength and coverage gaps
Respect privacy and control
Applicants choose which evidence to expose and can redact with audit trails. Hiring partners receive anonymized signals before requesting deeper dives.
The result is a fairer, more transparent hiring conversation—and a data engine we continue to evolve with governance at the center.