The "Model-Share" Framework: Quantifying Brand Visibility in Closed-Source LLMs

The Death of the Rank Tracker

For two decades, B2B marketing leaders have slept soundly relying on a single source of truth: the SERP rank. If your SaaS appeared in position one for "best marketing automation tools," you captured the demand. The metrics were deterministic, linear, and visible.

In 2026, that certainty has evaporated.

With the rise of Answer Engines (Perplexity, SearchGPT) and closed-source LLMs (Claude, Gemini), the user journey has shifted from query-click-read to query-answer-act. A potential buyer asking ChatGPT, "What is the best AI content automation tool for GitHub blogs?" does not see a list of ten blue links. They see a synthesized recommendation, often highlighting only one or two "winners."

If your brand is not part of that synthesis, you are invisible.

This presents a massive analytics blind spot. Traditional SEO tools cannot crawl the probabilistic "black box" of an LLM. There is no publicly accessible index to scrape. To survive this shift, marketing leaders must adopt a new framework for measurement: Model-Share.

What is the Model-Share Framework?

Model-Share is a methodology for quantifying a brand's presence within generative AI outputs. It moves beyond keyword rankings to measure Entity Confidence—how thoroughly an AI model understands who you are, what you do, and why you are the authority in your niche.

At its core, Model-Share answers three critical questions that determine your visibility in the generative era:

1. Retrieval Probability: When a user asks a relevant non-branded query, how often does the model include your brand in the answer?
2. Sentiment Alignment: When the model discusses your brand, is the context accurate, favorable, and aligned with your positioning?
3. Citation Density: In hybrid engines (like Perplexity or Google AI Overviews), how frequently is your content cited as the source of truth?

This is not just about "ranking." It is about becoming part of the model's parametric memory and its retrieval-augmented generation (RAG) sources.

The Three Pillars of Model-Share

To operationalize this framework, we must break down "visibility" into measurable components. Just as you track backlinks and domain authority for SEO, you must track these three pillars for GEO (Generative Engine Optimization).

1. Share of Recommendation (SoR)

This is the most direct parallel to traditional market share. It measures the frequency with which your brand appears in "Listicle" or "Best of" queries generated by AI.

• The Test: If a user prompts, "List the top 5 enterprise GEO platforms for B2B SaaS," does your brand appear? Are you first, third, or absent?
• The Mechanism: LLMs generate these lists based on probabilistic association. If your brand is frequently mentioned alongside "best," "top rated," and specific industry terms in the model's training data (or retrieved context), your SoR increases.
• Optimization Strategy: This requires high-volume, authoritative content distribution. Tools like Steakhouse help by automating the creation of long-form, entity-rich comparison articles that feed these associations into the digital ecosystem.

2. Entity-Attribute Association

Does the model know what features you offer? This is crucial for long-tail, feature-specific queries.

• The Context: A user might ask, "Which AI writing tool supports markdown export to GitHub?"
• The Gap: You might be a leading AI writer, but if the model hasn't firmly connected the entity "[Your Brand]" with the attribute "Markdown Export," you will be excluded from the answer.
• The Fix: You need explicit, structured content that clearly maps features to your brand entity. This is where Schema.org markup and clear, declarative sentences in your documentation and blog play a massive role.

3. Citation Share of Voice

For Answer Engines that browse the live web (Perplexity, Bing Chat, Google AI Overviews), visibility is driven by information gain. The engine looks for unique data points, quotes, or frameworks to construct its answer.

• The Metric: How often is your URL cited as a footnote or source link?
• The Driver: Unique data. Generic content gets synthesized without attribution. content with proprietary stats, unique frameworks (like this "Model-Share" concept), or contrarian viewpoints gets cited.

Traditional SEO vs. Model-Share (GEO)

The transition from SEO to GEO requires a fundamental shift in how we structure content and measure success.

How to Measure Model-Share: A Practical Workflow

Since we cannot look "under the hood" of GPT-4 or Claude 3.5 Sonnet, we must use probabilistic probing. This involves treating the LLM as a user and running systematic tests.

Step 1: Define Your "Golden Queries"

Identify the top 20–50 questions your bottom-of-funnel prospects are asking. Do not just use keywords; use full natural language queries.

• Legacy SEO: "AEO software pricing"
• GEO Query: "What is the most cost-effective AEO software for startups that integrates with GitHub?"

Step 2: The "Incognito" Probe

Run these queries through the major models (ChatGPT, Gemini, Claude, Perplexity) using a fresh instance or API to avoid personalization bias.

Record the results based on a simple scoring matrix:

• Mentioned (1 point): Brand is listed among others.
• Recommended (3 points): Brand is explicitly recommended as the best option.
• Sole Answer (5 points): Brand is the only solution mentioned.
• Hallucinated (0 points): Brand is mentioned but with incorrect details.
• Absent (-1 point): Brand is not mentioned.

Step 3: Analyze the "Why"

If you are absent, analyze the competitors who did appear.

• What entities are they associated with?
• Do they have a specific "vs" page that the model is referencing?
• Is their documentation more accessible to crawlers?

Strategies to Increase Model-Share

Once you have quantified your baseline, you need to move the needle. This is where Generative Engine Optimization (GEO) comes into play.

1. Flood the Context Window with Structured Data

LLMs love structure. They thrive on clear relationships between entities. To increase your Model-Share, your website must be a pristine source of structured knowledge.

• JSON-LD Schema: Implement comprehensive schema markup (Product, FAQ, Organization, SoftwareApplication). This speaks the robot's language directly.
• Markdown-First Publishing: LLMs are often trained on code and markdown repositories. Publishing content in clean markdown (rather than heavy, script-laden HTML) can improve ingestibility. This is why platforms like Steakhouse prioritize markdown-to-GitHub publishing workflows—it aligns perfectly with how AI models consume text.

2. The "Co-Occurrence" Strategy

Models learn through association. If the word "Steakhouse" frequently appears in the same paragraph as "Enterprise GEO Platform" and "Content Automation," the model statistically binds these concepts together.

• Tactic: Create content clusters that aggressively link your brand name to your target category keywords grammatically.
• Example: Instead of just saying "We help with content," say "Steakhouse is the leading AEO platform for marketing leaders looking to automate long-form content."

3. Optimizing for "Zero-Shot" Answers

Your content should be written to be extracted. This means using the Inverted Pyramid style at the passage level.

• Start with the Answer: Every section of your blog post should begin with a bold, direct answer to the heading's question.
• Follow with Data: Immediately support the answer with a statistic or a list.
• End with Nuance: Save the fluff for the end.

This structure makes it incredibly easy for an Answer Engine to grab that specific paragraph and serve it as the result, citing you as the source.

Advanced Tactics: Agent-Ready Content

We are moving toward an agentic web, where AI agents will browse on behalf of users. These agents don't read; they process goals.

To capture Model-Share in an agentic world, your content must be executable.

• Clear Pricing Pages: Agents need to know costs to make recommendations. Hidden pricing kills Model-Share.
• API Documentation: If you are a B2B SaaS, your docs are your marketing. Agents look for capabilities defined in API references to verify if a tool can solve a user's technical problem.

Common Mistakes That Kill Model-Share

Even sophisticated marketing teams fail at GEO because they apply old SEO logic to new AI problems.

• Mistake 1: Keyword Stuffing. LLMs detect pattern anomalies. Stuffing keywords lowers the "perplexity" (unpredictability) of text in a bad way, often flagging it as low-quality spam. You need Entity Density, not keyword density.
• Mistake 2: Ignoring Brand Positioning. If your website says you are "The Uber for X" but your press releases say you are "The Airbnb for X," the model gets confused. Inconsistent training data leads to hallucinations or exclusion.
• Mistake 3: Gating All High-Value Content. If your whitepapers and case studies are behind PDFs and login walls, the LLM cannot read them. If the LLM cannot read them, it cannot learn from them. You must ungate your core knowledge to train the models.

Conclusion: The Race for the "Default Answer"

The winner of the next decade of B2B marketing will not be the brand with the most backlinks. It will be the brand that becomes the default answer in the world's AI models.

Model-Share is the metric that matters. It represents your digital footprint in the collective intelligence of the internet. By focusing on structured data, entity clarity, and high-volume, high-quality content generation, you can train these models to recognize your brand as the authority.

This is a volume and precision game. It requires a content engine capable of producing depth at scale. Whether you build an internal newsroom or leverage AI-native automation platforms like Steakhouse, the goal remains the same: Teach the AI who you are, so it can tell the world.