Think of Spotted Zebra as a smart hiring co‑pilot that understands candidates’ skills, helps you run better interviews, and tells you who is most likely to succeed in a role – instead of just guessing from CVs.
Traditional hiring relies heavily on CVs, gut feel, and inconsistent interviews, leading to poor hires, bias, and long time‑to‑fill. Spotted Zebra uses skills assessments and AI interview intelligence to standardize evaluation, surface best‑fit candidates faster, and improve quality of hire.
Proprietary skills ontology and assessment content combined with accumulated performance outcome data, embedded into recruiter and hiring manager workflows, creating switching costs and continuously improving prediction quality.
Hybrid
Structured SQL
Medium (Integration logic)
Model performance and fairness depend on quantity and diversity of historical hiring and performance outcome data; scaling across roles, industries, and geographies requires continuous revalidation of assessments and monitoring of bias.
Early Majority
Positions itself explicitly around ‘skills science’ and a rich skills taxonomy, combining structured skills assessments with AI‑supported interview intelligence rather than just generic video interviewing or simple testing, enabling more rigorous skills-based hiring programs.