From pasted text to ready guidelines in 5 minutes
Enter your data
Paste a link to an already published article or a draft you plan to add to the site. Type in the keyword you want to win citations for in AI Search.
The algorithm analyzes your competition in Google and ChatGPT
In under 5 minutes CitationOne fetches and analyzes your content. At the same time it inspects the 10 top-ranking competitor pages and asks ChatGPT about the same phrase to see which sources the model names. The tool compares your material with the market leaders across 10 quality dimensions and E-E-A-T signals.
Receive a ready Before / After list
The system generates a complete report. You don't get generic advice - you get a precise action plan. The tool points to specific paragraphs to fix and gives ready structural guidelines for your editors.
What exactly does the tool analyze?
Each dimension is scored on its own - separate 0-10 score, separate problems and separate recommendations.
CSI Alignment
CSI AlignmentChecks whether the article answers exactly the question the user asked - not a similar one, but exactly that one.
How we measure it →Information Density
DensityMeasures how many facts are in the article. Generalities and empty sentences lower the score - concrete data and numbers raise it.
How we measure it →Knowledge Graph
EAVAI sees the article as a network of facts: Entity - Attribute - Value. The more complete the network, the higher the chance of citation.
How we measure it →BLUF
BLUFAI models favor articles that give the answer at the beginning of every section. Not at the end, not after an intro - right at the start.
How we measure it →Chunk Optimization
ChunkAI systems split articles into chunks before analysis. Each chunk should make sense without reading the whole article - it should be autonomous.
How we measure it →Cost of Retrieval
CoRThe harder it is for AI to find information in the text, the lower the chance of citation. Headings, lists and tables reduce this cost.
How we measure it →TF-IDF
TF-IDFChecks whether the article uses domain terminology. Lack of specialized terms signals to AI: "this author doesn't know the topic deeply".
How we measure it →Semantic Roles
SRLAI absorbs knowledge better when the article topic is an active subject in sentences - not a passive object described by others.
How we measure it →Fan-Out & AIO Coverage
Fan-OutEach query is effectively several sub-queries at once. We check how many your article covers - because AI uses exactly those sub-queries for synthesis.
How we measure it →Effort Score
EffortMeasures visible editorial effort: article length, images, video, tables and FAQ schema. AI models prefer polished content - not quick drafts.
How we measure it →E-E-A-T
E-E-A-TGoogle and AI models trust content backed by a real expert with experience. The report measures specific trust signals.
How we measure it →See whether - and why - you fall behind the leaders
CitationOne queries two independent sources about your phrase. From Google it fetches and analyzes the 10 top-ranking pages; ChatGPT it asks about that same phrase, checking whether your page shows up in the answer. You see the optimization gap against real competition in search and in AI Search at once.
- Tabular CQS comparison for every analyzed page
- Identify leaders and weak spots in the current SERP
- ChatGPT's answer to your phrase: whether you are cited and mentioned, and which pages the model gave as sources
- Analysis of structure and format of the top-rated content
Concrete recommendations with measurable CQS impact
Forget vague tips. CitationOne points to the exact content fragments that need optimization and provides ready “Before / After” versions. You see the estimated score uplift for each change, so you only roll out the fixes that build your authority in AI Search the fastest.
AI optimization is important because language models pick content based on their own criteria.
CitationOne measures 10 AI citation dimensions - each with estimated CQS impact and a specific fix ready to paste.
Google AI Overview is a synthesis of many sub-queries
AI Overview doesn't cite one article - it synthesizes answers to a dozen related sub-queries at once. The tool decomposes that synthesis and shows which sub-queries your content covers, and which gaps cause AI to skip you.
- AI Overview synthesis decomposed into sub-queries
- Coverage map: which sub-queries you handle
- Recommendations to fill the gaps with specific content
AI sees entities - not just keywords
Language models build a knowledge representation from facts - entities and their attributes. The report maps these relationships and shows which facts set you apart from the competition (Unique), which are must-have (Root) and which you're missing.
- Full entity table with Unique / Root / Rare classification
- Coverage map: covered / gap / unique
- Interactive knowledge graph in the app
A tool built for smooth team workflows
You can download and ship every audit instantly. PDF Report - a readable, jargon-free summary ready to send to your client. Markdown file - a ready set of structural guidelines for copywriters and editors. “Quick Wins” plan - a curated list of fixes that will lift content quality in AI's eyes in one go.
Ready to send
Markdown
For documentation
Instant fixes - right after the audit
Up to 7 ready-made fixes generated algorithmically. Each with a source badge and a link to the dimension that detected it. You know what to fix before you read the full report.
Structured data audit
Algorithmic analysis of schema.org JSON-LD. Detects 31 schema types, checks field completeness and flags missing required schemas with priority.
Article, FAQPage, Product, HowTo, Review, BreadcrumbList, WebPage, Organization, Person, AggregateRating - each with its list of required and recommended fields. Status: present / incomplete / missing. Google Rich Result eligibility.
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