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.

A group of colleagues collaborating in a bright office with a laptop and notes

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.

Content Quality
01Direct Answer Clarity
02Structured Heading Logic
03Context Depth
04Factual Density
05Readability for Summarization
Technical Readiness
06Entity Clarity
07Extractability
08Query Match Coverage
09Semantic Alignment
Trust & Authority
10Trust Signals
11Citation Worthiness
12Freshness Signals
13Actionability

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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