Public Methodology Specification
PULSE Method v1.0
PULSE (Position, Endorsement, Sentiment & Entity Performance) is a client-readable, evidence-based methodology for scoring corporate visibility across commercial AI engines.
Core Mathematical Formula
PULSE Method v1.0 Specification
Prompt Result Score = Position Factor × Recommendation Factor × Sentiment Factor
1. Position Factor
Evaluates presence and prominence rank order (e.g. 1st choice vs secondary mention).
2. Recommendation Factor
Measures endorsement strength (Strong Recommendation, Neutral, Conditional, or Negative).
3. Sentiment Factor
Assesses tonal sentiment, factual accuracy, and absence of hallucinated complaints.
Reliability Ratings:HighMediumIndicative
The PULSE Score reflects observed AI recommendation performance within a controlled prompt set and testing period. It does not measure every possible answer or guarantee future visibility.
Recommendation Classes
- Strong Recommendation: The AI engine explicitly names the entity as a top-recommended solution.
- Neutral Mention: The entity is listed among alternatives without explicit commercial endorsement.
- Conditional Endorsement: Recommended only for niche sub-contexts or under specific caveats.
- Negative / Risk Mention: Listed alongside complaints, regulatory warnings, or hallucinated defects.
Reliability Ratings
- High Reliability: Evaluated across 20+ prompts, 5+ repeat runs per prompt, and 4 platforms over 7+ days.
- Medium Reliability: Evaluated across 10–19 prompts with 3+ repeat runs.
- Indicative Reliability: Initial snapshot evaluation. Never presented as definitive.
Illustrative Scoring Matrix
Illustrative Example — Not Client Data| Prompt Scope | Position | Endorsement | Sentiment | Result |
|---|---|---|---|---|
| "Best MSP in London" | 1st Choice (1.0) | Strong (1.0) | Positive (1.0) | 1.00 |
| "Compare London MSP vendors" | 2nd Choice (0.8) | Neutral (0.6) | Positive (1.0) | 0.48 |