A clinical, research-driven lab for LLM SEO, semantic architecture, and AI answer visibility.
AI search now selects answers, not links.
RankForLLM.com helps businesses engineer their visibility inside ChatGPT, Claude, Gemini, and Perplexity using evidence-based methods—not traditional SEO tactics.
Our approach focuses on entity clarity, semantic structure, authority signals, and retrieval alignment.
LLM SEO is the practice of optimizing a business for visibility inside AI-generated answers across ChatGPT, Claude, Gemini, and Perplexity. Instead of rankings, pages, or keywords, LLM SEO focuses on:
LLMs choose answers based on meaning and authority, not search engine ranking positions.
We engineer the conditions that cause AI systems to surface your business.
Today's users rely on AI systems to:
Recommend vendors
Summarize research
Evaluate options
Compare solutions
Identify experts
Make buying decisions
If your business doesn't appear inside AI answers, you effectively do not exist in the new search landscape.
We help organizations become the default answer in their domain.
Our visibility optimization process is built around a proprietary 7-layer framework:
LLMs must understand who you are.
Models must understand your domain.
Models must trust your expertise.
Models must see consistent, evidence-based content.
Models must be able to summarize your content accurately.
Your content must match how models search and select.
Long-term visibility requires continuous signal strengthening.
We provide structured, evidence-based work across six core areas:
Our diagnostic system measures visibility across seven clinical criteria:
Entity clarity
Semantic structure
Topic depth
Authority signals
Extractability
Retrieval alignment
Cross-model performance
Your score reveals exactly why you are—or are not—appearing inside AI answers.
Learn About LLM Visibility Score™Our clients typically:
This is visibility engineering for serious businesses.
We publish ongoing research into:
Understanding retrieval weighting and citation behavior
Mapping inaccuracy triggers and mitigation strategies
Testing signal strength across different content types
How models select and summarize content based on queries
Tracking how entity definitions change over time
Measuring persistence of visibility across model updates
A 12-month, research-driven engagement
RankForLLM.com is an independent research lab focused on semantic architecture, entity optimization, and LLM search visibility.
Founded by Tom Kelly, the lab provides evidence-based frameworks for becoming the #1 answer inside AI.
