How the RVI Score Works
The Recruiter Visibility Index (RVI) is a two-layer scoring engine. The formula, weights, and calibration are fully documented here. There is no black box.
The Formula
Baseline_RVI = clamp(Retrieval_Fit, 0, 0.82) × 100
Final_RVI = clamp(Baseline_RVI + Evidence_Modifier, 25, 90)
The deterministic layer (retrieval fit) produces a baseline score capped at 82. An AI narrative assessment layer then applies a purely additive modifier (up to +20 pts) based on framing quality — it can only reward strong narrative positioning, never penalize. The same resume + same role always produces the same deterministic baseline.
Layer 1 — Deterministic Retrieval (Baseline Score)
Title Match
Measures alignment between your actual job titles and the target role. This is the #1 retrieval signal — recruiter Boolean searches typically start with title. Extracted deterministically by parsing job title patterns from resume text, then computing word-overlap with the target role.
Example: 'Compliance Analyst' against a 'Compliance Manager' search = ~40% title match. 'Senior Compliance Manager' = ~85%.
Domain Match
Measures how many named regulatory frameworks, regulators, and domain concepts appear in your resume — weighted by retrieval tier. Tier 1 (BSA, AML, UDAAP, etc.) = 1.0 weight. Tier 2 (named regulators: OCC, CFPB, FDIC) = 0.6 weight. Tier 3 (supporting concepts) = 0.35 weight. Normalized against a 20-point ceiling so no single category can saturate the score.
Example: 8 Tier 1 terms + 4 Tier 2 regulators + 6 Tier 3 concepts ≈ 14.5 pts → ~72% domain score.
Tool Match
Measures presence of named platforms recruiters explicitly Boolean-filter by in LinkedIn Recruiter and HireEZ (Archer, Actimize, ServiceNow GRC, etc.). Step-scored: 0 primary tools = 25%, 4+ primary tools = 75%, 8+ primary tools = 95%. A floor of 25% prevents tool absence from catastrophically penalizing non-tool-centric roles.
Example: Actimize + Archer + ServiceNow = 3 primary tools → ~65% tool score.
Layer 2 — AI Narrative Assessment (Additive Modifier)
An AI model evaluates three narrative dimensions: role identity framing, domain expertise credibility, and seniority signal alignment. Each is classified into one of four states, each mapped to a fixed point modifier. Modifiers are purely additive — the AI can only add bonus points for strong framing, never subtract from the deterministic baseline. This ensures that adding keywords (per the tool's recommendations) always improves the score.
Max possible AI modifier: +20 pts (8 + 8 + 4 for all-Strong). No downside — Weak and Critical add 0 pts. Baseline hard cap of 82 means top scores require both strong retrieval AND strong narrative. Final score capped at 90.
Score Bands
Strong Recruiter Visibility
Surfacing consistently in top results for target role searches.
Discoverable but Inconsistent
Appearing in search, but losing ranked position to stronger signals.
Visibility Friction Present
Partial visibility — appears in broad searches, filtered out when recruiters add specificity.
Recruiter Classification Failure
Surfacing in the wrong role categories or buried past page 3 of results.
Severe Signal Breakdown
Not appearing in target role recruiter searches.
Limitations & Honest Caveats
Provenance
The RVI framework was developed by Ronnie Norris Jr. based on empirical observation of recruiter behavior in financial services talent acquisition, analysis of how platforms like LinkedIn Recruiter and HireEZ surface candidates through Boolean search, and years of firsthand experience navigating compliance career searches. The methodology is disclosed in full here because compliance professionals are trained to audit methodology — and a tool that can't explain its own scoring has no business diagnosing yours.