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Methodology
Methodology

The signals behind every score.

Open methodology for job verification, scam detection, and employer trust scores. This page documents what we measure, how the model behaves, and where the limits are — no black boxes.

Inputs
URL · Text · Email
Model class
LLM + heuristics
Output
Score 0–100 + signals

What we analyze

  • Payment requests

    Mentions of fees, deposits, equipment costs, or training charges borne by the applicant.

  • Off-platform pressure

    Push to WhatsApp, Telegram, Signal, or personal email — particularly early in contact.

  • Company verifiability

    Domain age, public footprint, executive presence, and consistency with claimed scale.

  • Recruiter authenticity

    Identity signals, employment history coherence, and historical posting patterns.

  • Posting linguistics

    AI-generated, copy-pasted, or template patterns common to high-volume scam networks.

  • Compensation outliers

    Salary, equity, or benefits significantly above market for the stated role and seniority.

  • Process irregularities

    Unusually fast offers, missing interview steps, or pressure to commit within hours.

  • Data harvesting cues

    Requests for SSN, banking info, or government IDs prior to a verified offer.

How a score is produced

  1. 1
    Normalize input
    URLs are fetched server-side with a 5s timeout and 1MB cap. Text input is sanitized. Personal data in pasted content is not persisted beyond your scan record.
  2. 2
    Extract signals
    The model classifies content against the indicator set above, returning structured evidence — not free-text conclusions.
  3. 3
    Aggregate score
    Indicators are weighted into a 0–100 composite. Bands: Low (0–34), Elevated (35–64), High (65–100).
  4. 4
    Return recommendations
    Specific, actionable next steps — verify the recruiter, request a counter-signal, decline the off-platform move.

Limitations

  • Probabilistic, not judicial. Scores estimate risk patterns. They are not findings of fraud and must not be used as the sole basis for any employment decision.
  • Coverage gaps. Closed platforms (LinkedIn private postings, internal ATSes) can't be fetched server-side. Paste the content instead.
  • Adversarial drift. Scam patterns evolve. We retrain regularly and rely on community reports to surface new patterns.
  • False positives exist. Legitimate roles can trip indicators (high comp, fast process). Always weigh evidence in context.

Saw a pattern we should learn from?

Submit a report. Confirmed patterns retrain the model.

Report a job