About Proven Cite
The intelligence layer for AI-era search
Proven Cite helps businesses assess content readiness and observe answers from configured AI models. Our assessments do not reveal providers’ internal ranking decisions.
Our Mission
Search is being rewritten. We're here to help you win.
When users ask ChatGPT, Claude, Gemini, or Perplexity a question, these AI systems don't return a list of links. They synthesize an answer and cite their sources. The content that gets cited isn't always the highest-ranking page. It's the content that LLMs can most effectively process, trust, and extract from.
Traditional SEO tools weren't built for this new reality. They measure keyword rankings and backlink profiles, not how an LLM's retrieval pipeline evaluates your content for citation worthiness. That gap is what Proven Cite was built to close.
Our mission is to give every business transparent, explainable insight into how AI systems evaluate their content, and a clear roadmap to improve it.
Our Story
How Proven Cite came to be
The path from insight to platform.
The Insight
We realized that the content getting cited by ChatGPT, Claude, and Perplexity wasn't always the highest-ranking page. It was the content LLMs could most effectively process, trust, and extract from.
The Gap
Every SEO tool on the market measured keyword rankings and backlink profiles. None of them measured what actually determined whether an LLM would cite your content.
The Framework
We built the 13-factor AI Readability Index: a rules-based scoring framework mapped to documented LLM behaviors, including retrieval patterns, chunking mechanics, trust estimation, and citation selection logic.
The Platform
Proven Cite combines content readiness assessments with recorded model tests. The two forms of evidence are distinct.
What Sets Us Apart
Built differently from the ground up
Every design decision in Proven Cite maps back to how LLMs actually work.
13-Factor AI Readability Index
A proprietary, rules-based scoring framework mapped to documented LLM behaviors. Every factor has a clear rationale grounded in how retrieval-augmented generation actually works.
Transparent & Explainable
No black boxes. Every score comes with evidence from your actual content, a clear explanation of why it matters, and specific recommendations to improve.
Built for GEO, Not SEO
Generative Engine Optimization is a fundamentally different discipline. We measure extractability, entity clarity, factual density, and citation worthiness, not keyword rankings.
Content Assessment and Recorded Tests
Our engine assesses fetched content against 13 factors. These rubric scores are separate from saved AI responses and do not predict revenue or guarantee citations.
The Framework
The 13 factors behind every score
Each factor maps directly to documented LLM behavior. Organized into three categories that mirror how AI systems evaluate content.
Our Values
What drives everything we build
Transparency
Every score has evidence. No black boxes, no guesswork. You see exactly what we see and why it matters.
Rigor
Our 13 factors are grounded in documented LLM behaviors, retrieval-augmented generation research, and real-world testing.
Accessibility
Enterprise-grade analysis accessible to every content team. No technical setup, no code installation, no consulting required.
Impact
Every recommendation maps to a specific page and factor. Prioritized by effort and impact so your team always knows where to start.
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