> ## Documentation Index
> Fetch the complete documentation index at: https://docs.anymorph.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# How GEO Works

> How AI engines choose brands to cite and how Anymorph measures visibility

## How AI Engines Find and Cite Brands

When a user asks an AI assistant a question, the response doesn't come from a single search. The engine runs a multi-stage pipeline that transforms one question into dozens of searches, retrieves hundreds of candidate pages, then discards 97–98% of them before generating a cited answer.

Understanding this pipeline is key to GEO. Each stage has different filtering criteria, and your content needs to survive all of them to get cited.

### Stage 1: Query Fan-Out

The engine doesn't search your exact question. It decomposes it into **8–12 parallel sub-queries**, each targeting a different angle: definitions, comparisons, pricing, recent developments, expert opinions, alternatives.

For example, "What's the best CRM for small teams?" might become:

* "best CRM software small business 2026"
* "CRM comparison small teams pricing"
* "CRM features for teams under 50"
* "HubSpot vs Salesforce small business"
* and several more variants

Only 27% of these sub-queries are stable across repeated searches (iPullRank, 2025). This means **content breadth matters more than single-keyword depth**. A page that covers multiple angles of a topic has more chances to be retrieved by at least one sub-query.

<Note>
  Google AI Mode fires hundreds of sub-queries for complex "Deep Search" requests using a custom Gemini model specifically designed for query fan-out (Google, May 2025).
</Note>

### Stage 2: Web Retrieval

Each sub-query hits the engine's internal search backend:

| Engine                 | Search Backend                                                                   |
| ---------------------- | -------------------------------------------------------------------------------- |
| **ChatGPT**            | OpenAI's own search index (transitioned from Bing in 2024–2025, now primary)     |
| **Perplexity**         | Proprietary index (built in-house since 2023)                                    |
| **Google AI Overview** | Google Search index + Knowledge Graph                                            |
| **Gemini**             | Google Search index                                                              |
| **Claude**             | Undisclosed (Anthropic has not publicly confirmed which search provider is used) |

This stage retrieves **dozens to hundreds of candidate documents** across all sub-queries combined. The exact number varies by engine and query complexity. At this point, your page just needs to be findable by one of the sub-queries. Traditional SEO signals (domain authority, backlinks, keyword relevance) still influence this step.

### Stage 3: Semantic Ranking and Filtering

The engine runs multi-layer machine learning ranking on all retrieved documents:

1. **Semantic similarity scoring**: Documents are compared to the original query using embedding models. Pages with cosine similarity of 0.88+ get 7.3x more citations (Wellows, Dec 2025).
2. **E-E-A-T gate**: A binary pass/fail filter. 96% of eventually-cited content passes this gate; below-threshold content is excluded regardless of relevance. Author credentials, schema markup, and domain reputation matter here.
3. **LLM re-ranking**: A separate model re-ranks surviving documents at the **passage level**, not the page level.

After this stage, \~50–100 candidates remain. This is where the pool drops most dramatically.

### Stage 4: Passage Extraction

AI engines don't read entire pages. They extract specific passages of **134–167 words** (the optimal cited passage length from large-scale analysis). What determines which passage gets selected:

* **Information density**: Factually dense, no preamble or filler
* **Self-containment**: The passage answers the sub-query without needing surrounding context
* **Structural signals**: Headings, FAQ blocks, comparison tables, and definition boxes signal chunk boundaries to the parser
* **Position on page**: 44.2% of all citations come from the **first 30%** of a page's content. Content in the bottom 10% earns only 2.4–4.4% of citations (AirOps, 2026)

This is why the [Quotability](/concepts/geo-strategy) attribute (self-contained answer capsules after each H2) matters so much. It directly addresses how passage extraction works.

<Tip>
  The "answer in the first 100 words" pattern (BLUF) is not a writing style preference. 90% of top-cited content follows it because passage extractors evaluate early content first and assign it higher relevance scores.
</Tip>

### Stage 5: Prompt Assembly (Pre-Citation Embedding)

This is the most misunderstood stage. **Citations are not added after the response is written.** They are structurally embedded into the LLM's prompt *before* it generates a single token.

The orchestration engine assembles:

* Citation markers linked to specific source URLs
* Ranked passage excerpts from surviving documents
* Publication dates and authority metadata
* Contradiction resolution between conflicting sources

The LLM then generates prose that is architecturally bound to these pre-embedded sources. It cannot invent a URL it was not given.

### Stage 6: Response Generation

The final LLM generates the response, constrained by the assembled evidence. Each inline citation number corresponds to a specific passage excerpt from Stage 4.

**The result**: Out of all initially retrieved candidates, only **5–15 are cited** in the final response. The vast majority of retrieved content is discarded.

### Why This Matters for GEO

Each pipeline stage requires different content qualities:

| Stage              | What your content needs                    | GEO lever                                            |
| ------------------ | ------------------------------------------ | ---------------------------------------------------- |
| Fan-out            | Broad topical coverage                     | Cover multiple query angles per page                 |
| Retrieval          | Findability by search engines              | Technical SEO, schema markup                         |
| Semantic ranking   | High relevance to query                    | Intent-aligned headings, direct answers              |
| E-E-A-T gate       | Trust signals                              | Author credentials, verifiable data, structured data |
| Passage extraction | Self-contained, information-dense passages | Answer capsules, BLUF pattern, FAQ blocks            |
| Prompt assembly    | Fresh, authoritative, non-contradictory    | Temporal grounding, entity consistency               |

A page that's strong at Stage 2 (good SEO rank) but weak at Stage 4 (no extractable passages) will be retrieved but never cited. This is exactly the gap that [GEO strategy](/concepts/geo-strategy) addresses.

## What Drives AI Citations

Research across 129,000+ domains and 680M+ citations reveals six key factors:

<AccordionGroup>
  <Accordion title="1. Referring Domain Authority" icon="link">
    The strongest predictor of AI citations. Brands with deeper, higher-trust backlink profiles are cited far more often. This overlaps with traditional SEO but carries even more weight in GEO.
  </Accordion>

  <Accordion title="2. E-E-A-T Signals" icon="shield-check">
    Experience, Expertise, Authoritativeness, and Trustworthiness. AI systems prioritize content that demonstrates real-world outcomes and verifiable expert credentials.
  </Accordion>

  <Accordion title="3. Content Freshness" icon="clock-rotate-left">
    Cited pages skew newer. Explicit dates ("Last updated: 2026-04-07") and current statistics signal freshness to AI engines.
  </Accordion>

  <Accordion title="4. Entity Consistency" icon="fingerprint">
    Consistent brand name, positioning, and facts across your website, social profiles, directories, and third-party sources reduce ambiguity for AI retrieval.
  </Accordion>

  <Accordion title="5. Third-Party Mentions" icon="comments">
    Independent mentions on Reddit, G2, Trustpilot, and industry publications are disproportionately present in AI-cited sources. Even unlinked brand mentions contribute.
  </Accordion>

  <Accordion title="6. Structured Data" icon="code">
    Organization, Product, FAQ, and Review schema markup helps AI engines extract and verify brand facts during retrieval.
  </Accordion>
</AccordionGroup>

### What You Can Actually Influence

Of these six factors, some are within your direct control and some require longer-term investment:

| Factor                   | Your control level   | How to improve                                                                                                                                 |
| ------------------------ | -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| **Domain Authority**     | Indirect (long-term) | Publish quality content, earn backlinks naturally, fix technical SEO issues in [SEO Health](/dashboard/seo-health)                             |
| **E-E-A-T Signals**      | High                 | Add author credentials, case studies, verifiable data to your pages                                                                            |
| **Content Freshness**    | High                 | Keep pages updated with dates, refresh statistics quarterly                                                                                    |
| **Entity Consistency**   | High                 | Align brand info across website, profiles, and directories via [Brand Settings](/settings/brand)                                               |
| **Third-Party Mentions** | Low (and risky)      | Organic community presence; paid/automated approaches carry [significant risks](/concepts/geo-strategy#external-channels-powerful-but-fragile) |
| **Structured Data**      | High                 | Add schema markup, FAQ blocks, logical heading hierarchy                                                                                       |

The factors you can directly control (E-E-A-T, freshness, entity consistency, structured data) are exactly what [GEO page optimization](/concepts/geo-strategy) targets. This is why content optimization delivers measurable results faster than third-party strategies.

## Engine-Specific Behavior

Each AI engine has different source preferences:

| Engine                 | Primary Sources                                                        | Strategy                                |
| ---------------------- | ---------------------------------------------------------------------- | --------------------------------------- |
| **ChatGPT**            | OpenAI search index, Wikipedia heavily weighted                        | Structured content + Wikipedia presence |
| **Perplexity**         | Proprietary index, Reddit heavily weighted, real-time search           | Community engagement + fresh content    |
| **Google AI Overview** | Google organic results (strongly correlated with traditional rankings) | Traditional SEO + Schema markup         |
| **Claude**             | Undisclosed, E-E-A-T signals                                           | Fact-based, authoritative content       |
| **Gemini**             | Google Search index                                                    | Google SEO + structured data            |

<Tip>
  Optimizing for multiple engines simultaneously is more effective than targeting a single one. Anymorph tracks all engines in parallel so you can identify cross-engine opportunities.
</Tip>

## How Anymorph Measures Visibility

### PAWC (Position-Adjusted Word Count)

Anymorph uses PAWC, the same metric from the Princeton KDD 2024 research paper, as its core visibility measurement:

```
positionWeight = exp(-relativePosition / (mentionSpan - 1))
PAWC = wordCount × positionWeight
```

This means a brand mention at the **top** of an AI response is weighted exponentially higher than one at the bottom. It's not just about being mentioned; it's about **where** you appear.

### GEO Score (0–100)

Your overall AI visibility is expressed as a **GEO Score**, a weighted composite of six sub-scores:

| Sub-score             | Weight | What it measures                        |
| --------------------- | ------ | --------------------------------------- |
| **Visibility**        | 30%    | PAWC-based visibility index             |
| **SOV Rank**          | 20%    | Share of Voice ranking vs competitors   |
| **Sentiment**         | 15%    | Positive vs negative brand mentions     |
| **Citation Coverage** | 15%    | % of citations pointing to your domain  |
| **Consistency**       | 10%    | Uniformity across different AI engines  |
| **Intent Coverage**   | 10%    | Visibility spread across search intents |

### Grade Scale

| Grade | Score | Meaning                                                |
| ----- | ----- | ------------------------------------------------------ |
| **A** | 80+   | Strong AI position. Maintain and expand.               |
| **B** | 60–79 | Good visibility. Target 1-2 areas for optimization.    |
| **C** | 40–59 | Average. Systematic GEO strategy needed.               |
| **D** | 20–39 | Low visibility. Foundational content work needed.      |
| **F** | \< 20 | Nearly invisible to AI. Full strategy review required. |

### Share of Voice

Anymorph calculates SOV in two ways:

* **PAWC-based SOV**: Academically precise, considers mention position and length
* **Mention-based SOV**: Intuitive count of brand mentions vs competitors

Both are displayed in the [Visibility dashboard](/dashboard/visibility) to give you complementary perspectives.

## Benchmarks

### SOV Benchmarks

* **\< 5%**: Nearly absent from AI responses
* **5–15%**: Present but lacking authority
* **15–30%**: Competitive presence
* **30%+**: Category leader in AI responses

### Citation Drift

Monthly citation variation of \~55% is **normal**. AI engines are non-deterministic, so some fluctuation is expected. Consistent trends over 30+ days are more meaningful than day-to-day changes.

## Next Steps

<CardGroup cols={2}>
  <Card title="GEO Strategy" icon="bullseye" href="/concepts/geo-strategy">
    Learn the 9 attributes that make content citable by AI.
  </Card>

  <Card title="Visibility Dashboard" icon="eye" href="/dashboard/visibility">
    See your GEO Score and metrics in action.
  </Card>
</CardGroup>