Share of Model Framework: Architectonics of Measuring AI Visibility, Narrative Control, and ROI in the Agentic Web Era

Section 1. The Epistemological Shift: From Information Retrieval to Knowledge Synthesis

The contemporary digital ecosystem is undergoing its most profound transformation since the advent of algorithmic ranking in the late 1990s. We are witnessing a fundamental epistemological shift from the paradigm of Information Retrieval (where the goal was finding a relevant document) to the paradigm of Knowledge Synthesis, where artificial intelligence acts as an active intermediary that interprets, aggregates, and generates new meaning. This transition marks the end of the "ten blue links" era and the dawn of the "Answer Era," requiring a radical rethinking of digital presence strategies, success metrics, and business operating models.

In the traditional SEO (Search Engine Optimization) model, the user performed the cognitive load: browsing SERP results, evaluating sources, filtering information, and synthesizing their own conclusions. The search engine's role was limited to indexing and ranking links. In contrast, in the new reality, Generative Engines such as ChatGPT, Perplexity, Claude, and Google Gemini take on this cognitive load. They function not as librarians, but as analysts providing a finished solution. This creates an environment where brands compete not for a click (traffic), but for the right to be part of the synthesized "truth" the model broadcasts to the user.

The loss of direct website contact due to the "zero-click" phenomenon is an existential threat to business models built on traffic monetization. Research indicates that users increasingly settle for AI answers without navigating to source sites, particularly in e-commerce and informational queries. In this context, the old Share of Voice (SoV) metric, which measured ad noise or organic visibility in static lists, becomes irrelevant. It is being replaced by Share of Model (SoM) — a metric determining a brand's share in the neural network's "mind," its presence in training data, and its priority in the context window during response generation.¹

1.1. Physics of the "Black Box": Vector Space and RAG

Understanding the need for SoM requires grasping the technical nature of modern LLMs. Unlike databases that store information in tables, LLMs operate in a multidimensional vector space. Words, concepts, and brands are converted into numerical vectors (embeddings). The proximity of these vectors determines semantic relationships. If your brand's vector is distant from the vector for "best CRM systems," the model will never mention you, regardless of the keyword density on your site.²

Most modern systems use RAG (Retrieval-Augmented Generation) architecture. This process consists of three stages: retrieving relevant fragments (chunks), ranking them, and feeding them to the model to generate an answer.² In this architecture, visibility depends not on meta-tags but on "semantic density" and content "quotability." The model acts as a probabilistic machine choosing the next token based on context. Your task is to maximize the statistical probability of your brand name appearing in a positive context.

1.2. Share of Model vs. Share of Search

It is critical to distinguish between Share of Search (SoS) and Share of Model (SoM). Share of Search measures consumer interest in a brand based on search query frequency in Google. It is a demand indicator driven by external factors (ads, PR).³ Conversely, Share of Model measures brand visibility inside AI responses to categorical queries. It is a supply indicator shaped by the algorithm.

For example, if a user asks, "What are the alternatives to Salesforce?", Share of Model shows how often HubSpot or Pipedrive are mentioned in ChatGPT's answer. This is a metric of influence at the Consideration stage, whereas Share of Search is often an Awareness or Intent metric.¹ In a world where 82% of B2B purchases are influenced by AI responses⁴, ignoring SoM means losing control over a critical decision-making point.

Section 2. KPI System: Multi-Level Measurement Architecture

To operationalize the Share of Model Framework, one must abandon a one-dimensional approach. Visibility in AI is not binary (present/absent); it is spectral. A brand may be mentioned but in a negative context, or mentioned as a secondary example. Therefore, the KPI system must encompass four dimensions: Presence, Positioning, Quality, and Technical Efficiency.

2.1. Presence Metrics (Inclusion & Visibility KPIs)

This level answers the basic question: "Does the brand enter the model's field of view?"

2.1.1. Share of Model (SoM)

An aggregated metric reflecting the share of brand mentions in responses to a representative sample of category queries compared to competitors.

• Calculation: (Count of Brand A Mentions / Total Mentions of All Brands in Sample) × 100%.
• Context: If in 100 answers about "best sneakers," Nike appears 40 times, Adidas 30, and your brand 5, your SoM is approx. 6.6% (5/75). This is a direct analog to market share in AI attention.⁵

2.1.2. Presence Rate

A granular metric showing the probability of a brand appearing in a single specific generation.

• Formula: (Number of responses with brand mention / Total test runs) × 100%.
• Application: Used to identify "blind spots." For instance, a brand might have a 90% Presence Rate for "CRM for startups" but 0% for "Enterprise CRM." In the Sereda.ai case study, this metric rose from 0.3 to 9.0 (on a 10-point scale) after optimization.²

2.1.3. Recommendation Rate

The most valuable metric for commercial brands. It distinguishes a simple mention ("Brand X exists") from an active recommendation ("We recommend Brand X").

• Criteria: The brand appears in a numbered list (Listicle), is bolded as a "Top Pick," or described using superlatives ("best," "most effective").
• Weight: In scoring models, a recommendation carries 3-5x the weight of a simple mention as it directly influences conversion.⁶

2.1.4. Citation Rate

In RAG systems (Perplexity, Bing, SearchGPT), models are required to provide sources.

• Definition: The percentage of answers where the brand's domain or controlled assets (blog, docs) are cited as a clickable source link.
• Strategic Value: This is a "trust" metric. A high Citation Rate means the algorithm perceives the brand's content as "Ground Truth." Research shows citations can drive significant high-quality referral traffic.⁷

2.2. Positioning and Prominence Metrics

Not all mentions are equal. A mention in the first sentence carries significantly more weight than a footnote.

2.2.1. Average Position Rank

For list-generating queries (e.g., "Top 10 tools"), position is decisive.

• Methodology: 1st place = 1 point, 2nd = 2 points. Lower is better. In the Sereda.ai case, the brand moved from complete absence to consistently ranking in the Top 3, often #1.²
• Nuance: Users scan AI answers similarly to SERPs (F-pattern), so the top 3 positions capture the lion's share of attention.

2.2.2. Answer Depth Score

A qualitative metric evaluating the detail of the brand description.

• Scale (0-10):
• 0: No mention.
• 1-3: Name only or brief mention.
• 4-7: Description of core function.
• 8-10: Detailed description of benefits, USP, and use cases.
• Value: Indicates how deeply the model "understands" the product. High depth correlates with higher conversion probability as the user gets enough info to decide without further searching.⁶

2.3. Narrative Integrity and Safety Metrics

The biggest risk in GEO is losing control of the message. AI can hallucinate or broadcast obsolete data.

2.3.1. Accuracy Score

The percentage of factual statements about the brand that are true.

• Components: Price accuracy, feature availability, geographic coverage.
• Example: If the model claims a free tier exists when it was sunsetted a year ago, Accuracy Score drops. In Sereda.ai research, accuracy rose from 2.5 to 9.0 after implementing an AI Page.²

2.3.2. Hallucination Rate

The share of responses containing fabricated or harmful information.

• Methodology: Measured via Fact-Checking a sample of responses. Critical for Brand Safety. High hallucination rates indicate a lack of clear data in the model's Knowledge Graph.⁸

2.3.3. Sentiment Score

Evaluation of the emotional framing of the mention.

• Specifics: AI usually strives for neutrality, so "negative" often appears as caveats ("however, users complain about...") or unfavorable comparisons.
• Classification: Positive (recommendation), Neutral (description), Negative (criticism). Goal: maximize positive mentions.¹⁰

2.3.4. LLM Confidence Score

A technical metric showing how "confident" the model is in its answer.

• Mechanics: Based on analyzing token log-probabilities (logprobs). If a model generates a brand name with low probability, it may "hedge" the answer with words like "possibly," "some sources suggest." High Confidence Score is achieved through data consistency across global and local levels.¹²

2.4. Comparative Metrics Table

Metric	Type	What it Measures	Benchmark Target	Source
Share of Model (SoM)	Quantitative	Market share in AI "mind"	> 25% (Leader), 10-20% (Challenger)	14
Citation Rate	RAG / SEO	Source authority	15-30%	14
Recommendation Rate	Conversion	Strength of endorsement	> 50% of mentions	6
Accuracy Score	Qualitative	Truthfulness	> 90%	2
Sentiment Score	Reputation	Tone	> 80% positive	14
Hallucination Rate	Risk	Generation errors	< 5%	8

Section 3. Methodology: Active Probing Process

Measuring Share of Model is impossible via passive methods (like Google Analytics). It requires active intervention—"probing" models by simulating user behavior. Since we lack access to OpenAI or Google internal logs, we use a "black box" method: input (prompts) -> analysis of output (answers).

3.1. Phase 1: Semantic Core Construction (Query Set)

Measurement quality depends on question quality. The Query Set must cover the full Customer Journey and account for AI interaction specifics. It is recommended to divide queries into four strategic clusters:²

1. Navigational & Brand (Direct Inquiry): Checks factual knowledge about the brand.
   • Examples: "What is?", "Who founded?", "Is safe?".
   • Goal: Establish baseline "Entity Knowledge." If the model doesn't know who you are, it won't recommend you.
2. Category & Discovery: User seeks a solution without naming a brand.
   • Examples: "Best email marketing platforms", "Tools for HR automation".
   • Goal: Measure Inclusion Rate. This is the battleground for new customers.
3. Comparative & Evaluation: User chooses between options.
   • Examples: " vs [Competitor]", "Alternatives to Salesforce", "Pros and cons of".
   • Goal: Evaluate competitive positioning and model argumentation.
4. Transactional & Constraint-Based (Long-Tail): Complex queries with conditions, typical for AI.
   • Examples: "Find a CRM under $50/mo with Slack integration and Ukrainian interface".
   • Goal: Test E-GEO optimization for attributes. Research shows AI is most useful (and prone to hallucinations) here.²

3.2. Phase 2: Testing Protocol & Variable Isolation

To obtain scientifically valid (replicable) data, one must minimize noise and randomness inherent in generative models.

• Context Isolation: Every test must run in a new, "clean" session (New Chat) or via API with temperature=0 (or near zero) to ensure deterministic outputs. Using "Incognito" mode helps avoid personalization history bias.⁵
• Multi-Model Testing: Relying only on ChatGPT is insufficient. Test:
  • RAG Systems (Perplexity, Bing, SearchGPT): Fresh data and citations are critical here.
  • Parametric Models (GPT-4o, Claude 3.5, Gemini Pro): Fundamental training knowledge is critical here.
  • Hybrid Systems (Google AI Overviews): Mix of search index and generation.²
• Replication: Since models are stochastic, run each prompt 3-5 times and average the results. This levels out random "glitches" (Reducing LLM Noise).⁵

3.3. Phase 3: Scoring (Human-in-the-Loop vs. Automated)

Turning text answers into numbers.

• Manual Audit: Effective for deep analysis. In the Sereda.ai case, a 0-10 scale was used for 5 criteria (Presence, Accuracy, Features, No False Info, Position), scored by two independent researchers.²
• LLM-as-a-Judge: For scaling, use one model (e.g., GPT-4) to evaluate another's answers against criteria. This automates processing thousands of queries.¹⁵

3.4. Control Measurements (A/B Testing)

To prove GEO efficacy, conduct controlled experiments:

• Before/After: Measure metrics before changes (Baseline) and 30-60 days after (e.g., post llms.txt launch).
• Control Group: Compare optimized pages/products vs. unoptimized ones.
• Denial Tests: The Sereda.ai study used blocking access to the AI Page to confirm improvements were caused by the new content, not a general model update.²

Section 4. Benchmarks: Landmarks in the Fog

The GEO market is nascent, but empirical data allows setting clear success benchmarks.

4.1. Quantitative SoM & Visibility Benchmarks

• Category Dominance: Market Leaders typically reach 35-45% Share of Model. They appear in nearly every second answer.¹⁴
• Strong Challenger: 20-30% range. A healthy position for competing brands.
• Emerging Brand: Initial success is 5-15%. Below 5% is effective invisibility ("digital ghost").
• Inclusion Gap: Research shows vast disparity: one startup may have 5% visibility while a GEO-optimized competitor has 80%.²

4.2. E-GEO Optimization Tactics Efficiency

The E-GEO study² on 7000+ e-commerce queries provides unique data on tactic effectiveness:

Tactic (Heuristic)	Visibility Lift	Mechanism
Quotation Addition	+41%	Transformers attend heavily to text containing authority references and expert quotes.
Statistics Addition	+37%	Numbers reduce entropy/uncertainty, making the answer more "factual" for the model.
Cite Sources	+28%	Explicitly citing sources increases Trustworthiness.
Fluency Optimization	+24%	Grammatically perfect, readable text requires less compute to process and integrate.
Keyword Stuffing	-10% (Drop)	Unlike old SEO, keyword spam degrades semantic quality and is penalized as "noise".2

4.3. Time-to-Impact

GEO is faster than SEO but slower than PPC.

• 10 days: Minimum for initial indexing signs of new specialized content (AIpage) by Bing/Google crawlers feeding RAG.²
• 30-60 days: Period of significant visibility growth. In Sereda.ai, Presence rose from 0.3 to 9.0 specifically over 60 days. This covers re-indexing, vectorization, and cache updates.²
• Long-term: Parametric knowledge updates (becoming part of the model's "brain") require retraining cycles (6-12 months).

4.4. "Big Brand Bias" Phenomenon

Research confirms "Big Brand Bias." In YMYL (finance, health), models prefer info from high-authority third parties (Forbes, Healthline) over a brand's own site. For a bank to appear in "best deposits," getting cited on NerdWallet is more effective than optimizing its own page. This shifts strategy from "Owned Media" to "Earned Media".²

Section 5. ROI Model: Impact Economics in the AI Era

Traditional ROI models based on Last Click attribution fail in generative search. If AI answers a user and persuades them, but they visit via Direct Traffic later, classic analytics miss the GEO contribution. A new model is needed — RoGEO (Return on Generative Engine Optimization).

5.1. Multi-Level RoGEO Structure

ROI is calculated as the sum of impacts across three levels.⁴

Level 1: Direct Performance

• High-Intent Traffic: AI-Referral Traffic volume may be lower than organic, but quality is higher. Users arrive with formed intent. Conversion can be 2-10x higher than traditional search.¹⁷
• Lower CAC: GEO lowers Cost Per Lead by 30-50% vs. paid channels due to organic "AI recommendations".⁴

Level 2: Brand Impact

• Leading Indicator: Strong correlation exists between Share of Search/Model and future Market Share. SoM growth today predicts sales growth in 6-12 months.¹
• Halo Effect: AI recommendations often drive users to verify via Google. A lift in Branded Search is indirect proof of GEO success.¹⁸

Level 3: Operational Efficiency

• Support Cost Reduction: Quality AIpages enable agents to auto-answer client queries, deflecting support tickets.¹⁶
• Resource Savings: Content prepped for GEO is a ready-made knowledge base for sales and onboarding.

5.2. Calculation Formulas

1. Basic RoGEO:
   RoGEO = \\frac{(\\text{Revenue from AI-Referrals} + \\text{Brand Lift Value}) - \\text{GEO Costs}}{\\text{GEO Costs}} \\times 100\\%

   ⁴

2. Cost of Inaction (COI):

   Alternative risk metric. If competitors hold 80% SoM and you hold 5%, you lose access to a market segment.

   COI = (\\text{Total Category Query Volume}) \\times (\\text{Lost SoM \\%}) \\times (\\text{Avg Order Value})

   Ignoring machine logic creates an \"existential threat\".²

Section 6. Operating System: "Single Source of Truth" Technology

Managing Share of Model requires a clear operating system combining infrastructure, content, and governance.

6.1. Technical Infrastructure: AIpage & llms.txt

The core concept is "Single Source of Truth" (SSOT) — a specialized entry point for AI agents providing verified data in a format they understand.²

• AIpage: A web page optimized purely for machine reading. Stripped of visual "noise" (CSS, JS, ads), which constitutes 80% of code and blurs semantic vectors. Contains structured facts: pricing, features, specs. In Sereda.ai, launching this on a subdomain catalyzed visibility growth.²
• llms.txt Standard: The "robots.txt for AI." A Markdown file at root (domain.com/llms.txt). It acts as a system prompt for crawlers (GPTBot, ClaudeBot), providing a company summary and links to "clean" Markdown pages. This saves crawl budget and ensures freshness.²
• Structured Data: Schema.org (Organization, Product) and sameAs properties linking to Knowledge Bases (Wikidata, Crunchbase) anchor the brand in the Knowledge Graph.²

6.2. Content Engineering: Writing for Machines

Transitioning to "content engineering":

• Inverted Pyramid (BLUF): "Bottom Line Up Front." The main answer must be the first sentence. AI attention weights are highest at the start.²
• High Quotability: Saturate text with unique stats, data, and expert quotes. High "Semantic Density" — minimum fluff, maximum entities.²
• Structure: Tables increase citation probability by 2.5x.²

6.3. Processes & Governance

• Owner: Role evolves to AI Visibility Manager or Head of Digital Reputation. Responsible for llms.txt and AIpage accuracy.
• Update Cadence:
  • AIpage/llms.txt: Update with every product/price change. Bots scan text files frequently.²
  • Monitoring: Weekly SoM and Citation Rate checks. Quarterly narrative audits.⁷
• Workflow: "AI-Check" step before publishing: "Will a machine understand this? Are there citeable facts?"

Conclusion and Strategic Recommendations

We stand at the threshold of a new digital competition era. Share of Model is not just a metric; it's the currency of influence. Brands ignoring this shift risk becoming invisible to modern search tools and high-value clients.

Key Steps:

1. Audit: Measure baseline Share of Model across 4 query types. Find blind spots.
2. Infrastructure: Deploy llms.txt and AIpage. A Quick Win for immediate signaling.
3. Content: Shift strategy. Less fluff, more data, tables, and citations.
4. Measurement: Integrate SoM, Citation Rate, and Accuracy Score into executive reporting. Move beyond clicks.

The window to establish "Ground Truth" status in neural networks is open. Those who start systemic Share of Model work today will gain disproportionate advantage in the Agentic Web of tomorrow.

Sources

1. Share of Model vs. Share of Voice: The New KPI for B2B SaaS Growth - SteakHouse Blog, accessed January 15, 2026, https://blog.trysteakhouse.com/blog/share-of-model-vs-share-of-voice-geo-kpi
2. The Evolution of Digital Visibility: From Search Engine Optimization (SEO) to Generative Engine Optimization (GEO) — A Comprehensive Analytical Report.docx
3. Share of Search: How to Calculate, Use, And Improve It - Ahrefs, accessed January 15, 2026, https://ahrefs.com/blog/share-of-search/
4. 2025 Guide To Measuring B2B Generative Engine Optimization (GEO) ROI |, accessed January 15, 2026, https://abmagency.com/2025-guide-to-measuring-b2b-generative-engine-optimization-geo-roi/
5. Share of Model: a key metric for AI-powered search - Hallam, accessed January 15, 2026, https://hallam.agency/blog/share-of-model-a-key-metric-for-ai-powered-search
6. Measuring Share of Voice Inside AI Answer Engines - Single Grain, accessed January 15, 2026, https://www.singlegrain.com/artificial-intelligence/measuring-share-of-voice-inside-ai-answer-engines/
7. AI search era: Calculating ROI of answer ownership strategies - Relixir - The AI Generative Engine Optimization GEO Platform, accessed January 15, 2026, https://relixir.ai/blog/blog-ai-search-era-calculating-roi-answer-ownership-strategies
8. HalluLens: LLM Hallucination Benchmark - arXiv, accessed January 15, 2026, https://arxiv.org/html/2504.17550v1
9. Hallucination | DeepEval by Confident AI - The LLM Evaluation Framework, accessed January 15, 2026, https://deepeval.com/docs/metrics-hallucination
10. Tracking AI Sentiment: Using GA4 to Measure Brand Perception in LLMs - Goodish, accessed January 15, 2026, https://goodish.agency/tracking-ai-sentiment-using-ga4-to-measure-brand-perception-in-llms/
11. Brand Presence | Adobe LLM Optimizer - Experience League, accessed January 15, 2026, https://experienceleague.adobe.com/en/docs/llm-optimizer/using/dashboards/brand-presence
12. The LLM Confidence Score: How Global Truth Validates Local Content for Maximum AI Visibility - Seonali, accessed January 15, 2026, https://www.seonali.com/blog/llm-confidence-score-global-local-ai-visibility
13. Confidence Unlocked: A Method to Measure Certainty in LLM Outputs - Medium, accessed January 15, 2026, https://medium.com/@vatvenger/confidence-unlocked-a-method-to-measure-certainty-in-llm-outputs-1d921a4ca43c
14. AI Visibility Benchmarking: Track & Beat Competitors - Passionfruit SEO, accessed January 15, 2026, https://www.getpassionfruit.com/blog/ai-visibility-benchmarking-competitors-guide
15. LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide - Confident AI, accessed January 15, 2026, https://www.confident-ai.com/blog/llm-evaluation-metrics-everything-you-need-for-llm-evaluation
16. The Definitive ROI Model for Investing in Generative Engine Optimization - Hashmeta.ai, accessed January 15, 2026, https://www.hashmeta.ai/blog/the-definitive-roi-model-for-investing-in-generative-engine-optimization
17. how do you prove roi on generative engine optimization efforts? - Reddit, accessed January 15, 2026, https://www.reddit.com/r/content_marketing/comments/1q75n1x/how_do_you_prove_roi_on_generative_engine/
18. Measuring Brand Impact in an LLM-First world: A Practical Framework for Visibility Without Clicks​ - Gravity Global, accessed January 15, 2026, https://www.gravityglobal.com/blog/measuring-ai-and-zero-click-impact-on-brands

To see how this Share of Model framework plays out in practice, review the Sereda.ai case study on how a single AI Page changed ChatGPT and Gemini recommendations.

If you’re looking for a more compact, practitioner-friendly introduction to GEO, AIEO, and AI Pages, read the overview article AI GEO, AIEO, and AI Page.

For deeper implementation detail on RAG chunking, vector embeddings, and schema that operationalize this model, continue with the GEO + RAG technical report.