About LLMs Network
LLMs Network independently measures how AI companies appear in AI-generated answers, using a fixed query set and a published methodology. Our current production measurement covers one AI system (Perplexity Sonar Pro); additional systems are on our roadmap.
Our Vision
We started as an AI company directory — a curated index of GEO agencies, AI SEO tools, LLM infrastructure providers, and more. But the problem we kept seeing was deeper than discoverability.
Millions of users now ask AI models directly — "What are the best AI tools for X?" or "Which companies offer Y?" — and most companies have no measurement of how they are described in those answers.
Our vision is to evolve from a directory into an AI Citation Intelligence Platform — giving companies the frameworks, benchmarks, and data infrastructure to systematically track their AI visibility.
LAVF Framework
LLMsNetwork AI Visibility Framework — five evidence-based dimensions of AI citation performance, grounded in peer-reviewed GEO research.
Authority
Domain credibility signals that AI models use to evaluate trustworthiness — backlink quality, brand search volume, editorial mentions, and earned-media presence across authoritative sources.
Brand search volume correlates 0.334 with AI citation rate (80M citation analysis, ConvertMate)
Citation Quality
The accuracy, consistency, and reliability with which AI models cite your brand. Includes factual fidelity, citation recurrence across queries, and absence of hallucinated or misattributed claims.
25–30% inconsistency in LLM citations across equivalent queries (arXiv:2603.09296)
Content Structure
Structural and semantic features that make content machine-readable for LLMs — formatting, schema markup, clear heading hierarchy, and authoritative signals proven to increase citation rates.
Specific structural features statistically increase LLM citation rates (arXiv:2603.29979, GEO-SFE)
Knowledge Coverage
Breadth and depth of topic coverage that positions a brand as a comprehensive source — the range of queries for which AI models consider your content relevant and cite-worthy.
Sites with 1.16M+ traffic receive 6.4× more citations per query vs. sites under 3K traffic (SE Ranking)
AI Accessibility
Technical and structural barriers that prevent AI crawlers and indexers from accessing content — crawlability, page speed, structured data completeness, and LLM-friendly content delivery.
88% of Google AI Mode citations sourced from outside regular SERP top results (Moz 2026)
Research Foundation
The LAVF framework and our measurement methodology are grounded in peer-reviewed academic research, starting with the Princeton GEO paper published at ACM SIGKDD 2024, complemented by the latest industry measurement data.
Foundational Research
Aggarwal et al. (Princeton University) introduced the GEO framework at ACM SIGKDD 2024, demonstrating up to 40% improvement in AI visibility through structured content optimization. This research established that AI citation behavior is measurable and systematically improvable — the empirical basis of the LAVF framework.
Academic Papers
GEO: Generative Engine Optimization
Aggarwal et al. — Princeton University
The seminal paper that introduced the GEO framework. Demonstrated up to 40% improvement in AI visibility through structured content optimization — the academic foundation of the LAVF framework.
Earned Media Bias in AI-Generated Responses
Examines systematic bias toward earned media sources in LLM citation behavior, with implications for brand visibility strategies.
LLM Citation Bias: Sources and Patterns
Characterises citation bias patterns across major large language models, identifying structural and topical skews in what gets cited.
Citation Failures and Source Inconsistency in LLMs
Finds 25–30% inconsistency in LLM citations across equivalent queries — a key reliability challenge for GEO practitioners.
AgenticGEO: Optimizing Visibility in Agentic AI Systems
Extends GEO methodology to agentic AI workflows where multi-step reasoning and tool use introduce new citation dynamics.
GEO-SFE: Structural Features and Their Effect on AI Citations
Identifies specific structural content features (formatting, schema markup, authoritative signals) that statistically increase LLM citation rates.
Feature-Level GEO: Granular Optimization for AI Visibility
Provides a feature-level breakdown of GEO signals, enabling fine-grained content optimization targeted at specific AI model citation patterns.
Industry Data
What We Do
- →AI Company Directory
A curated, publicly accessible directory of AI companies and tools across every major GEO and AI search category.
- →LAVF Visibility Assessment
Structured measurement of a company's AI citation performance across all five LAVF dimensions, benchmarked against category peers.
- →GEO Research & Benchmarks
Systematic tracking of how AI companies appear in AI-generated answers, with data-driven reports on citation patterns and coverage gaps. Current production measurement covers one AI system (Perplexity Sonar Pro).
- →AI Citation Intelligence Platform (Roadmap)
Real-time monitoring of brand mentions and citations across ChatGPT, Claude, Gemini, Perplexity, and emerging AI search engines.
Founder
LLMs Network was founded by Chang Sup Woo, based in Sydney, Australia.
For enquiries, partnerships, or research collaboration, please reach out via the Contact page.
LLMs Network measures, monitors, and analyses AI visibility — we do not guarantee specific visibility outcomes.