Today, despite modern hiring tools, companies and recruiters still rely on scattered notes, memory, and subjective opinions across multiple interview rounds, often spread across emails, documents, and disconnected systems. Studies show that 74% of companies admit to making bad hires, and up to 80% of employee turnover is driven by poor hiring decisions, highlighting the unreliability of current evaluation processes.
This is unacceptable because interview feedback remains inconsistent, context is lost between rounds, and decisions are influenced more by bias and individual judgment than structured evidence. With 58% of hiring teams struggling to assess candidate skills accurately and 40% reporting interviewer bias and inconsistency, the core breakdown lies in how interviews are evaluated and decisions are made.
We envision a world where every interview is captured, structured, and analysed in real time, where hiring decisions are backed by clear, comparable signals across all candidates, and where every stakeholder operates with full context and confidence. In this world, hiring is not a matter of intuition, but a repeatable, evidence-driven process that reduces bad hires and improves long-term outcomes.
We are bringing this world about through AI-powered interview intelligence, real-time transcription and analysis, structured evaluation frameworks, and a unified decision layer that transforms interviews into reliable, decision-ready data.
Efficiency
Quality
Adoption