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A rubric is the structured scoring criteria attached to an evaluation template. It’s how intervyo.ai turns a free-form voice interview into a defensible scorecard. Rubrics are versioned, calibrated by default, and applied identically to every participant — same dimensions, same weights, same bar.

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Anatomy of a rubric

{
  "dimensions": [
    {
      "id": "problem_decomposition",
      "label": "Problem decomposition",
      "weight": 0.30,
      "passThreshold": 6.5,
      "description": "Can the candidate break ambiguity into concrete sub-problems and pick a place to start?"
    },
    {
      "id": "code_quality",
      "label": "Code quality & correctness",
      "weight": 0.30,
      "passThreshold": 7.0
    },
    {
      "id": "tradeoff_thinking",
      "label": "Trade-off thinking",
      "weight": 0.25,
      "passThreshold": 6.0
    },
    {
      "id": "communication",
      "label": "Communication under pressure",
      "weight": 0.15,
      "passThreshold": 6.0
    }
  ],
  "overallPassThreshold": 7.0
}
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dimensions[].weight
number
Fractional weight in the overall score. Weights across all dimensions should sum to 1.0. The platform normalizes if they don’t, but explicit is better.
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dimensions[].passThreshold
number
Minimum score on this dimension for a pass. A candidate that scores below the threshold on any single dimension fails the rubric — even if their overall is above overallPassThreshold.
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overallPassThreshold
number
Minimum weighted-average score for a pass. Defaults to 7.0 if unset.

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How scoring runs

For each dimension, the AI:
1

Reads the transcript

Identifies the segments relevant to this dimension — code-quality evidence in the coding round, discovery-question evidence in a sales round.
2

Grades against the dimension's description

Produces a 0–10 score with a short reasoning paragraph citing the specific transcript lines that drove the score.
3

Joins into the breakdown

All per-dimension scores are bundled into evaluation_breakdown with transcript citations and reasoning per dimension.
4

Computes overall + pass

Weighted average → overall score. Compare against thresholds → emit passed: true|false.

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Why dimensions matter more than overall

A single overall score throws away the signal you need to actually decide. Two candidates can both score 7.5 overall and be wildly different hires:
CandidateCommunicationTechnicalTrade-offsAuthenticity
Alex9.06.57.59.0
Brin6.09.07.59.0
Both score 7.5. Alex is a strong PM-leaning IC; Brin is a strong heads-down engineer. The dimension breakdown is the hiring signal, not the average.
Show your hiring panel the per-dimension breakdown, not the overall. Decisions converge faster when everyone’s looking at the same evidence per dimension, with the transcript citations one click away.

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Templates ship with calibrated rubrics

You don’t have to design rubrics from scratch. intervyo.ai ships calibrated starters for the most-hired roles:

Software engineers

Problem decomposition · Code quality · Trade-off thinking · Communication

Sales reps

Discovery quality · Pitch adaptation · Objection handling · Closing

Customer support

Diagnosis · Written communication · Technical fluency · Escalation judgement

Product managers

Prioritization · Customer empathy · Cross-functional language · Metric literacy
See the full list of role templates. You can use them as-is, or fork them — every starter is a calibrated baseline, not a contract.

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Calibration analytics

Rubrics are useful only if humans use them consistently. intervyo.ai surfaces:
  • Recruiter-to-recruiter variance per dimension
  • Hiring-manager parity across panels reviewing the same candidates
  • Drift alerts when a recruiter’s scoring trends away from the team baseline by more than a threshold
  • Calibration sessions — built-in quarterly workflow where multiple scorers grade the same candidate and the platform surfaces divergence
Read more under Recruiter Calibration on the marketing site.

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Versioning

Rubrics on a template are versioned. Edits create a new version; existing sessions keep the rubric they were scored against. That way:
  • Hiring panels reviewing 60-day-old scorecards see the rubric that produced those scores
  • You can tighten a rubric mid-quarter without breaking historical comparability
  • Compliance teams can audit which version of which rubric scored which candidate
Don’t tweak a rubric and run a back-comparison expecting like-for-like numbers. A 7.5 under v3 of the rubric is not directly comparable to a 7.5 under v2 — the dimensions or weights changed.
Last modified on June 2, 2026