About this page

This page describes the Geonoxis team, mission, methodology, principles, and trust signals. Geonoxis is an independent Generative Engine Optimization scanner founded in 2025. We score websites against 23 measurable signals across four pillars — structured data, citation readiness, content authority, and brand entity — that correlate with citation by ChatGPT, Perplexity, Claude, and Google AI Overviews. We are not affiliated with any AI engine vendor.

Independent · Since 2025

The independent GEO scoring standard.

Geonoxis measures the 23 signals AI engines use to decide who gets cited inside generated answers. Built by search engineers, not vendors.

23
GEO signals scored
4
AI engines tracked
+312%
Avg citation lift
100%
Transparent scoring
Mission

Make AI citation measurable, fixable, and fair.

Search is moving from a list of blue links to AI-generated answers. The brands that get cited inside those answers win the new attention economy — the rest disappear. We built Geonoxis so any team can see, in plain English, why an AI engine is or is not citing their site, and fix it without guesswork.

No vendor partnerships. No paid score boosts. No black box. Just the signals that matter, measured the same way for everyone.

Principles

What we stand for

Transparent by default

Every score points at the raw data, the rule that ran, and the exact fix. No black-box magic numbers.

Engine-agnostic

We measure the signals that all major answer engines use — not the quirks of any single vendor.

Independent

No affiliate revenue from the platforms we measure. No paid score boosts. No partner conflicts.

Continuously updated

Scoring weights track real engine behavior. Every model change ships with a public changelog.

Methodology

23 signals. 4 pillars. One score.

We derive weights from observed citation behavior across ChatGPT, Perplexity, Claude, and Google AI Overviews. Signals are reweighted whenever a major engine behavior shift is measured. Every change ships to a public changelog.

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1

Structured data

28%

Schema.org coverage, JSON-LD validity, entity linking.

2

Citation readiness

26%

Extractable answers, FAQ shape, scannable structure.

3

Content authority

24%

E-E-A-T signals, authorship, freshness, source quality.

4

Brand entity

22%

Wikidata presence, knowledge graph signals, disambiguation.

Timeline

How we got here

  1. Q1 2025

    First scoring model

    Initial 14-signal model trained on observed ChatGPT citations.

  2. Q2 2025

    Engine expansion

    Added Perplexity, Claude, and Google AI Overviews. Model widened to 19 signals.

  3. Q3 2025

    Public beta

    Free scanner opened to the public. 50,000+ scans in the first quarter.

  4. Q4 2025

    v2 scoring model

    23 signals across four pillars. Public changelog and methodology docs.

Who we are

A small, focused team.

Geonoxis is built and operated by six search engineers and content strategists with prior experience at SEO platforms, NLP labs, and retrieval-system teams. We have no investors pushing us toward "premium placement" sales, because there is no such thing in AI citation.

3
Engineers
2
Strategists
1
Operators
Trust

Why you can rely on the score

  • No vendor lock-in
    Export your full report and underlying data anytime, in JSON or CSV.
  • Reproducible scoring
    Same URL, same model version, same score. We pin scoring runs to a model hash.
  • Open methodology
    Public docs describe every signal, the data we read, and the weight.
  • Privacy first
    We scan public URLs only. We don't store your visitor data and we don't drop cookies.
FAQ

About Geonoxis

Is Geonoxis affiliated with OpenAI, Google, Anthropic, or Perplexity?

No. Geonoxis is fully independent. We have no commercial relationship with any AI engine vendor and we do not sell guaranteed placement inside any AI answer.

Who built Geonoxis?

A small team of search engineers and content strategists with backgrounds in SEO, NLP, and retrieval systems.

How is the GEO score calculated?

We score 23 signals across four pillars: structured data, citation readiness, content authority, and brand entity. Each signal is weighted by its observed correlation with AI citation rates.

How often is the scoring model updated?

The model is reviewed monthly and updated whenever measurable engine behavior shifts. All weight changes are published to a public changelog.

Can I trust the score?

Every score is fully transparent. Every signal points at the raw data, the check that ran, and the exact fix. Nothing is hidden behind a black box.

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