Hiring for growing teams shouldn’t waste valuable time or rely on guesswork:
- Early stage teams spend hours screening and debating candidates with little consistency.
- Unstructured interviews and resume reviews miss the nuance that matters.
- Gut decisions work - until they don’t, causing lost momentum and costly mis-hires.
LayersRank is designed to address these pain points, giving founders and hiring leads the rigor and transparency needed to move faster and hire better.
LayersRank leverages Type-Reduced q-Rung Orthopair Fuzzy Numbers (TR-q-ROFNs) to produce multi-dimensional, confidence-weighted candidate rankings instead of simple pass/fail scores.
Each applicant is assessed across technical, behavioral, and contextual axes, generating explainable and auditable results that flow directly into actionable dashboards for your team.
LayersRank features an adaptive calibration loop, automatically updating scoring weights as new data from real-world team hiring accumulates.
Deeper methodology and technical details are available in our full whitepaper - just ask for access.