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.
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.
A taxonomy of citation failure modes; targeted repair raised citation rates by over 40% relative while modifying about 5% of content (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.
Structural feature engineering improved citation rate by 17.3% across six generative engines (arXiv:2603.29979)
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.
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.
Research Foundation
The LAVF framework and our measurement methodology are grounded in peer-reviewed academic research, starting with the GEO paper accepted at KDD 2024.
Foundational Research
Aggarwal et al. introduced the GEO framework in arXiv:2311.09735 (accepted to KDD 2024), reporting that GEO can boost visibility by up to 40% in generative engine responses, with effectiveness varying by domain. The paper also introduced GEO-bench, a benchmark of user queries across multiple domains.
Academic Papers
GEO: Generative Engine Optimization
Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan, Deshpande
Introduces GEO, a black-box optimisation framework for improving content visibility in generative engine responses, and GEO-bench for evaluation. Reports visibility gains of up to 40%.
Generative Engine Optimization: How to Dominate AI Search
Chen, Wang, Chen, Koudas
Large-scale controlled comparison of AI search and traditional web search. Reports a systematic bias in AI search toward earned media over brand-owned and social content.
Source Coverage and Citation Bias in LLM-based vs. Traditional Search Engines
Zhang, Ye, Peng, Garimella, Tyson
55,936 queries across six LLM-based and two traditional search engines. LLM engines cite more diverse domains, but do not outperform traditional engines on credibility, political neutrality or safety.
Diagnosing and Repairing Citation Failures in Generative Engine Optimization
Tian, Chen, Tang, Liu, Jia
A taxonomy of citation failure modes plus an agentic system that diagnoses and repairs them. Reports over 40% relative improvement in citation rates while modifying about 5% of content.
AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization
Yuan, Wang, Wang, Sun, Wang, Li
Evolves a diverse archive of optimisation strategies rather than applying fixed heuristics. Reports outperforming 14 baselines across 3 datasets on two generative engines.
Structural Feature Engineering for Generative Engine Optimization
Yu, Yang, Ding, Sato
Decomposes content structure into macro, meso and micro levels. Across six generative engines, reports a 17.3% improvement in citation rate and 18.5% in subjective quality.
Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibility
Liu, Xu
Optimises over interpretable structural, content and linguistic features instead of token-level rewriting. Finds citation behaviour is driven more by document-level properties than isolated lexical edits.
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.