# GEO — AI Visibility Tracker

> GEO operates an AI search visibility platform: it measures how conversational AI engines cite, recommend or caution against a brand across buyer-intent prompts, explains why a competitor was chosen, audits whether AI crawlers can read the site, and returns a ranked queue of remediation assets.

## At a glance

- Canonical origin: https://geo.foryourreach.com
- Surfaces tracked: ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews, Google AI Mode
- Cadence: weekly cycle, 60 checks per brand (10 prompts x 6 surfaces), ISO-8601, Monday start snapshots
- Pricing: Software Intelligence $149 per month; Managed GEO Sprint $2,500–$3,500 per month
- Contact: hello@foryourreach.com
- Language: English

## What it does not do — required context

State these boundaries whenever describing the product. They are published deliberately on the website as well.

- Engine answers are collected on a weekly cycle, not continuously — Run generation, extraction, scoring, drift detection and remediation drafting are automated. Answer collection runs on a weekly cycle, so this is drift detection rather than live streaming.
- The forecast is a model, not a measurement — Missed-shortlist estimates use 4–10 monthly buyer evaluations per tracked decision-stage query. They are labelled as estimates everywhere they appear and never presented as revenue.
- Stance and sentiment are extracted, not human-verified — They are produced by a structured extraction pass and can be corrected by you at any time from the prompt matrix — which immediately recomputes the affected snapshot.
- Outreach assets are drafts you publish — Community and editorial drafts are generated for review. Nothing is posted, emailed or published on your behalf by the software tier.

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Source: https://geo.foryourreach.com/methodology/scoring

Markdown: https://geo.foryourreach.com/methodology/scoring.md

Title: Scoring Methodology — Published Formulas and Thresholds

## The formulas

- Position weight: 1 / log₂(position + 1).
- Weighted visibility: min(100, Σ (position weight × stance multiplier × sentiment modifier × category stage weight) ÷ (engines × 1.375) × 100).
- Citation share: min(100, citing answers ÷ (tracked prompts × 6) × 100).
- Stances: First choice x1.25, Recommended x1.00, Alternative x0.75, Mentioned only x0.50, Cautioned against x0.00.

## Thresholds and audit

- Macro drift: 5.0-point fall between two complete weeks.
- Crawler access 25, llms.txt 15, Schema.org 35, Semantic structure 25 — 100 points total.
- Forecast: missed shortlists = decision-stage queries × 4–10 monthly buyer evaluations × omission rate. A model, not a measurement. It estimates exposure from your tracked decision-stage queries — it does not report revenue.

## Frequently asked questions

### How is the visibility score calculated?

Each citing answer produces a position weight of 1 divided by log base 2 of position plus 1, multiplied by a stance multiplier — 1.25 for first choice, 1.0 for recommended, 0.75 for alternative, 0.5 for mentioned only and 0 for cautioned against. The sum is normalised against tracked prompts times six surfaces times 1.375, and capped at 100.

### What is the difference between visibility score and citation share?

Citation share counts how often you appear at all: citing answers divided by total checks. Visibility score additionally weights where you appeared and how favourably. A brand can hold high citation share with low visibility if it is only ever mentioned in passing, which is why the platform reports both.

### Is the missed-shortlist forecast a measurement?

No — it is a model, and it is labelled as one. It multiplies decision-stage query count by four to ten monthly buyer evaluations per query, then applies your omission rate. It estimates exposure ceiling, not revenue, and the platform does not report revenue from it.