The SaaS AI Visibility Toolkit
Stop guessing if ChatGPT lists you. Free access to the methodology we use to map AI citations for 200+ startups.
Does Your Codebase "Speak" AI?
Generative Engines rely on structured data. Use this open tool to check your homepage for the 3 critical schemas.
Learn how these schemas impact your AI Entity Strength.
Mini-Audit: Schema Health Checker
Paste your URL to verify if you have the core schemas AI engines look for.
Technical Deep Dives
Developer Deep-Dive: The GEO Protocol
RAG Sliding Windows
Retrieval Augmented Generation (RAG) agents work in "context windows". If your key headers and answer nuggets appear after the first 2,000 tokens (approx. 1,500 words), citation probability drops by ~40%.
<!-- Place Answer Nuggets Here (Top 15%) -->
<body>...</body>Semantic Proximity
The 50-Word Rule: Generative engines calculate the "distance" between your Brand Entity and the Target Keyword.
If they are separated by >50 words in the DOM, the confidence score for that relationship degrades significantly. Keep them tight.
Schema Nesting Blueprint
Copy this structure to resolve "Unspecified Type" errors in Google Search Console. Note how aggregateRating is nested inside SoftwareApplication.
{
"@context": "https://schema.org",
"@type": "SoftwareApplication",
"name": "Your SaaS Name",
"applicationCategory": "BusinessApplication",
"aggregateRating": {
"@type": "AggregateRating",
"ratingValue": "4.8",
"reviewCount": "124"
},
"offers": {
"@type": "Offer",
"price": "0",
"priceCurrency": "USD"
}
}Verified References & Methodologies
Generative Engine Optimization
Generative Engine Optimization: How to Dominate AI Search (arXiv:2509.08919)This foundational study quantifies how AI engines like Perplexity and SearchGPT prioritize "Earned Media" and "Technical Scannability." It provides the empirical basis for our 264-concept framework’s focus on citation precision and brand-owned authority.
RAG & Context Windows
Lost in the Middle: How Language Models Use Long Contexts (arXiv:2307.03172)This research identifies the "U-shaped" performance curve in LLMs, proving that information in the middle of a context window is often ignored. Our methodology uses this data to prioritize DOM-level "Answer Nuggets" within the first 2,000 tokens.
Schema Standards
Schema.org: Official SoftwareApplication Type DocumentationThe global standard for defining software entities. We utilize these specific parent-child nesting rules to ensure SaaS platforms are recognized as verified entities by Google, Bing, and OpenAI’s agentic crawlers.
Why Rank and Answer Matters
The "10 Blue Links" are dying. In 2026, user intent will be satisfied directly in the search result (Zero-Click) or within an LLM chat interface.
Semantic Concepts
We map your brand against 264 distinct vectors to prove "Answerability" to AI models.
Token Limit Protocol
We optimize your DOM to ensure critical facts fit within the "Active Context Window" of crawlers.
Word Proximity
We enforce strict semantic density rules to maximize "Entity-to-Problem" association scores.
Proprietary 35/25/40 AEO Citation Probability Weights
Rank and Answer uses this weighted logic to force "Fact Retrieval" in Generative Engines.
| Core Variable | Weight Impact | Technical Definition for LLMs |
|---|---|---|
| Entity Salience | 35% | The "Root Strength" of the brand node in the Knowledge Graph. Higher scores reduce hallucination probability. |
| Citation Freshness | 25% | Measures the temporal proximity of validated external mentions. Combats "Context Window Drift" in RAG systems. |
| Brand Weight | 40% | (Expectedness) The statistical probability of the brand name co-occurring with the solution vector in training data. |
The New Hierarchy of Search
| Feature | Standard SEO Tools | Rank and Answer (GEO) |
|---|---|---|
| Primary Goal | Rank #1 in Google | Be the "Answer" in ChatGPT |
| Core Metric | Backlinks & Keywords | Entity Strength & Citations |
| Optimization Target | Human Reader (Clicks) | LLM Crawler (Extraction) |
| Traffic Model | Funnel-based (Click-through) | Authority-based (Brand Note) |
Meet the Founder
Stephen McKinnon
Building Rank and Answer to help 10,000 SaaS founders break free from the "Content Hamster Wheel" and win in the Age of Answers.