How Teamcast Measures
Our approach
What Teamcast actually examines in an interview, and why the result is a defensible measurement rather than a verdict.
Teamcast's core value is defensible measurement. A conversation with Maya is not graded on a gut feeling or on how polished the candidate sounds. Instead, the platform observes evidence of behavior in what the candidate actually says, maps each piece of evidence to a fixed, published competency framework, and scores it with deterministic math. The output is coverage — how well the interview evidenced each competency the role needs — and a human makes the decision.
What we examine
We look at observable behavioral evidence: the specific actions a candidate took, the decisions they made, the reasoning behind them, and the outcomes — described in their own words. We do not infer personality types, and we do not infer emotion or affect. Every conclusion is anchored to a concrete moment in the transcript that a reviewer can read back.
Three commitments
Evidence-based. Every signal we report is backed by an evidence unit that quotes the candidate — no unsourced scores.
Comparable. The competency framework is fixed and versioned, and role importance is applied consistently, so two candidates for the same role are measured on exactly the same axes.
Coverage, not a verdict. The deliverable says how well the interview covered each competency — sufficient, partial, or insufficient — never a hire / no-hire label.
How it fits together
Maya conducts the interview against a recruiter-approved plan. When the interview ends, the platform extracts evidence per signal from the transcript, scores it deterministically, and rolls it up into per-competency coverage plus highlights and lowlights — all cited. A recruiter then reviews the coverage and makes the call.