Platform

Candidate discovery built as infrastructure.

Finding the right VP candidate isn't a search problem. It's a signal problem. The candidates you need aren't applying. They're not on the first page of LinkedIn results. They require a different kind of search.

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The Problem

Job boards and LinkedIn find the wrong candidates fast.

Active candidates — the ones applying to job boards — represent a fraction of the executive talent pool. The best VP candidates are employed, not looking, and invisible to any tool that relies on active signals.

Majhi OS sources from passive signals. Employment tenure patterns, role transition timing, company growth indicators, network adjacency — signals that identify who is likely to move before they announce it.

Passive Candidate Identification

The sourcing engine identifies candidates who are statistically likely to be open to a move — based on tenure, company stage, role trajectory, and market conditions — not just those who clicked 'Open to Work'.

Ghost Job Detection

Before any candidate is sourced, the system checks whether the role is a ghost job — seat already filled, company restructuring, or role cancelled. We don't waste outreach on mandates that won't close.

ICP Scoring

Every candidate is scored against the mandate ICP before entering the pipeline. Industry, tenure, title trajectory, company size, funding stage — scored and ranked before any human review.

Signal Sources

Where the search engine looks.

The Majhi OS sourcing engine aggregates signals from multiple sources, weights them against the mandate ICP, and surfaces candidates ranked by fit score — before any outreach is written.

LinkedIn Signal Processing

Profile analysis beyond job titles — tenure patterns, promotion velocity, company stage at each role, skills trajectory. Processed against the mandate brief to produce a fit score before manual review.

Firmographic Filtering

Company-level signals: funding stage, headcount growth rate, revenue range, recent leadership changes. Used to identify candidates at companies where the move-probability is highest.

Network Adjacency

Second and third-degree network mapping to identify warm paths to high-fit candidates. Reduces cold outreach dependency and improves initial response rates before sequence optimisation.

Role Signal Analysis

Job posting analysis to identify companies actively restructuring their leadership layer — VP roles open for 60+ days, reposted roles, roles replacing departed leaders. High-signal sourcing targets.

The QMS Gate

No candidate enters the pipeline without clearing the quality gate.

Every candidate identified by the search engine passes through the Quality Management System gate before outreach is unlocked. The gate checks: ICP score threshold, evidence dossier completeness, ghost job clearance, and mandate fit confidence.

This is why shortlist approval rates run at 82%. Not because we send more candidates. Because fewer wrong candidates reach the hiring manager.

ICP Score Threshold

Minimum fit score required before the candidate advances. Threshold is calibrated per mandate based on role criticality and market depth. No exceptions without explicit override.

Evidence Dossier

Structured brief: proof points, risk flags, fit rationale, tenure analysis, compensation estimate. Every candidate arrives with a complete dossier — not a LinkedIn profile copy.

Confidence Gate

The system assigns a confidence score to each QMS pass. Low-confidence clearances are flagged for human review before outreach proceeds. High-confidence clearances advance automatically.

See Majhi OS on your actual mandate.

In 45 minutes, we map your hiring system's operational gaps — using your live mandate as working context, not a generic demo.

Book Your Mission Walkthrough →