Semarize
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SolutionRecruitment

Structured intelligence from every hiring conversation

From candidate screens to panel interviews, hiring teams generate critical conversational data every day. Semarize extracts structured evaluation signals - making interviews more consistent, scoring less biased, and hiring decisions more defensible.

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Structured interview scoringBias and compliance checksATS integration
SRecruitmentkit run

Kit

Interview Evaluation Kit

structured_question_askedboolean
competency_scorescore
bias_indicatorboolean
candidate_engagementscore
interviewer_talk_rationumeric

Output

{

"structured_question_asked": true,

"competency_score": 72,

"bias_indicator": false

}

Industry challenges

Hiring decisions rely on
unstructured impressions

Recruitment teams make high-stakes decisions from interviews - but rarely have structured, consistent data from those conversations.

Interview scoring is inconsistent

Different interviewers evaluate candidates differently. Without a structured rubric applied consistently, hiring decisions are vulnerable to bias and subjectivity.

Structured interviews aren't followed

Teams agree on an interview guide, but there is no way to verify which questions were actually asked across every candidate and interviewer.

Bias goes undetected

Off-script or non-compliant questions create legal and fairness risk. Spot-checking a few interviews never surfaces the pattern.

Candidate experience is unmeasured

The quality of how your team conducts interviews affects employer brand. Without evaluation, poor candidate experience goes undetected.

Use cases

Key use cases for
recruitment teams

Recruitment teams use Semarize across hiring quality, compliance, and people analytics - all through the same structured API.

QA & Compliance

Ensure interview processes follow legal requirements, DEI guidelines, and internal standards across every candidate interaction.

Enablement

Apply the same coaching-signal methodology to interviewer skill and feedback quality. Measure it at scale.

Customer Success

Track candidate and new-hire sentiment and engagement signals from talent conversations.

Data Science

Feed structured interview data into your people analytics platform. Correlate interview signals with quality-of-hire and retention.

How Semarize fits

Semarize connects to
your hiring stack

API-first integration with ATS and people analytics platforms. Structured signals flow into the systems your hiring team already uses.

Structured interview scoring

Score competencies, structured-question coverage, and candidate engagement against the same rubric for every candidate and interviewer.

Bias and compliance checks

Flag off-script or non-compliant questions with evidence spans, so hiring stays fair and defensible.

ATS integration

Push structured interview scores to Greenhouse, Lever, Workday, or any ATS. Consistent evaluation data attached to every candidate record.

People analytics feeds

Feed structured interview data into Visier, Culture Amp, or your analytics platform. Correlate interview signals with quality-of-hire and retention.

Bricks & Kits

Example Bricks for
recruitment

Industry-specific Bricks evaluate the dimensions that matter for recruitment & talent acquisition teams. Bundle them into Kits to create reusable evaluation frameworks.

structured_question_asked
boolean

Interviewer asked the pre-defined structured questions

→ true
competency_score
score 0–100

Candidate competency rating for a specific skill area

→ 72
bias_indicator
boolean

Potentially biased or non-compliant questions detected

→ false
candidate_engagement
score 0–100

Candidate engagement and enthusiasm level

→ 85
interviewer_talk_ratio
numeric

Proportion of the interview the interviewer was speaking

→ 0.48
role_requirements_covered
boolean

Key role requirements were probed during the interview

→ true

Interview Evaluation Kit

kit

Consistent, structured evaluation for every candidate interview.

structured_question_askedboolean
competency_scorescore
bias_indicatorboolean
candidate_engagementscore
interviewer_talk_rationumeric

Output

Structured signals, not summaries

Every evaluation returns deterministic JSON with typed values, reasons, and evidence spans. Same schema every time.

Interview evaluation response
{
  "run_id": "run_ghi789",
  "status": "succeeded",
  "output": {
    "bricks": {
      "competency_score": {
        "value": 72,
        "confidence": 0.86,
        "reason": "Strong technical depth, but limited systems design discussion",
        "evidence": ["...deep knowledge of distributed caching...", "...didn't address scalability..."]
      },
      "bias_indicator": {
        "value": false,
        "confidence": 0.93,
        "reason": "No potentially biased questions detected",
        "evidence": []
      },
      "candidate_engagement": {
        "value": 85,
        "confidence": 0.89,
        "reason": "Candidate asked multiple follow-up questions and expressed enthusiasm",
        "evidence": ["...I'm really excited about the team's approach...", "...can I ask about the roadmap?..."]
      }
    }
  }
}

Hire with structured data,
not gut feel.

Consistent interview scoring. Bias checks on every conversation. Evidence-backed evaluation for every candidate.

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