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Avric.ai

CANDIDATE INTELLIGENCE

Go beyond a match score. Understand why.

Avric.ai evaluates candidate information against the requirements of a specific role and surfaces structured evidence, strengths, gaps and context — so recruiters can make better-informed decisions.

PRODUCT FLOW

Core capability
JOB REQUIREMENTSRole requirements and evaluation context
AI ANALYSISResume is parsed and candidate information is evaluated against the role requirements
CANDIDATE EVIDENCEResume evidence, strengths, gaps and requirement coverage
SCORE & REASONINGWhy This Score, evidence vs inference and areas to validate
RECRUITER DECISIONScreening questions, next steps and candidate comparison
Avric.ai Candidate Intelligence — AI candidate summary, score transparency with requirement coverage 11/0/1, 95 Strong Excellent Match assessment and Client Submission Ready Strong Submit recommendation at 98% confidence

01 — WHY IT MATTERS

A score tells you how much. Intelligence helps explain why.

A single match score can rank a candidate, but it cannot defend a decision. Recruiters need the context behind candidate fit — demonstrated skills, experience alignment, gaps and the evidence supporting the assessment. Candidate Intelligence adds that context to recruiter decision-making.

02 — HOW IT WORKS

From resume and requirements to a decision you can explain.

  1. 01

    Resume + Job Requirements

    Candidate information is evaluated against the requirements of a specific role — not a generic profile.

  2. 02

    Structured Analysis

    Avric.ai analyzes alignment across skills, experience, domain and role context.

  3. 03

    Evidence & Context

    Findings are organized into strengths, gaps and the evidence behind them.

  4. 04

    Recruiter Decision Intelligence

    You receive recruiter-ready insight designed to support your judgment.

  5. 05

    Recommended Next Step

    The analysis closes with practical guidance for how to move the candidate forward.

03 — THE OUTPUT

What recruiters actually receive.

Every analysis is delivered as structured, recruiter-ready intelligence — not a number with no explanation.

Overall Match Intelligence

An overall view of candidate alignment with the role.

Required Skills Coverage

How candidate evidence aligns with important job requirements.

Experience Alignment

Context around relevant experience against the role.

Domain & Industry Relevance

Relevant domain or industry context, where evidenced.

Strengths

Areas of meaningful alignment with the role.

Gaps

Requirements that are missing, weak or not evidenced.

Evidence vs. Inference

What is directly supported by candidate data, and what is reasonable interpretation.

Screening Questions

Focused questions to investigate important gaps or uncertainties.

Recommended Next Step

Recruiter-oriented guidance based on the analysis.

Candidate Intelligence supports recruiter judgment. People make hiring decisions — Avric.ai helps them make better-informed ones.

04 — TRANSPARENCY

Know what's evidenced. Know what's inferred.

Candidate Intelligence helps recruiters distinguish information supported by candidate evidence from reasonable interpretation — so the basis behind every assessment is visible.

Avric.ai score transparency for candidate David Chen: requirement coverage — 11 fully evidenced, 0 partially evidenced, 1 not evidenced.Avric.ai requirement-level evidence for candidate David Chen: Python, FastAPI, PostgreSQL, MongoDB and Docker shown as evidenced requirements.

EVIDENCED

Information directly supported by the candidate's available profile and resume — demonstrated skills, stated experience and documented history.

INFERRED

Reasonable interpretation drawn from context — flagged so a recruiter can review and validate it rather than accept it as fact.

THE DIFFERENCE

More than resume matching.

Traditional resume matching can help surface alignment. Avric.ai goes further by giving recruiters the context behind the result — connecting requirements, evidence, strengths, gaps and recruiter-ready outputs in one candidate intelligence experience.

MATCH SCORE

YesAVRIC.AI
CommonTYPICAL

WHY THE SCORE

ExplainedAVRIC.AI
Often limitedTYPICAL

REQUIREMENT-LEVEL CONTEXT

Connected to candidate evidenceAVRIC.AI
VariesTYPICAL

STRENGTHS & GAPS

Surfaced for recruiter reviewAVRIC.AI
VariesTYPICAL

EVIDENCE VS INFERENCE

Clearly distinguishedAVRIC.AI
Often unclearTYPICAL

SCREENING QUESTIONS

Generated within the candidate workflowAVRIC.AI
Often separateTYPICAL

SUBMISSION CONTEXT

Recruiter-ready supportAVRIC.AI
Often manual or separateTYPICAL

CONNECTED RECRUITING WORKFLOW

ConnectedAVRIC.AI
Often separateTYPICAL

05 — WHY THIS SCORE

Don't just see the result. See what shaped it.

Recruiters can understand the factors contributing to a candidate assessment — so a recommendation never has to be taken on faith.

Avric.ai Why This Score panel for candidate David Chen: top strengths supporting the assessment.Avric.ai Why This Score panel for candidate David Chen: primary gaps that still need validation.
  • Requirement coverage
  • Strengths
  • Gaps
  • Relevant experience
  • Evidence
  • Areas requiring further validation

06 — IN PRACTICE

Intelligence that ends in an action.

Candidate Intelligence is not a report that sits in a folder. It supports what the recruiter does next.

Avric.ai match score breakdown for candidate David Chen: required skills match 100, experience match 95, domain and industry match 90, missing skills penalty 5, overall match score 95.
  1. Understand
  2. Review
  3. Validate
  4. Move Forward
  • Review candidate fit
  • Investigate gaps
  • Use focused screening questions
  • Prepare recruiter and client context
  • Continue the recruiting workflow

GET STARTED

Better candidate decisions start with better intelligence.

Bring structured evidence, context and recruiter-ready insight into every candidate decision with Avric.ai.