Source Analysis Dashboard
The Challenge
AI cites sources you can't see. Until now.
Every AI recommendation is built on a citation graph — a hidden network of sources that AI trusts. The brands that understand this graph earn citations. The brands that don't get left out of AI conversations entirely.
You don't know which sources AI trusts
AI models cite specific websites, articles, and datasets when generating recommendations. Without visibility into these citations, you have no idea what AI considers authoritative in your category — or whether your content is even on the radar.
Citation patterns are invisible to you
AI doesn't cite sources the way Google links to websites. Citations are embedded in how AI constructs its responses — drawing from training data, retrieval-augmented sources, and learned authority signals. Without dedicated analysis, these patterns remain completely hidden.
Competitors' content gets cited instead of yours
When someone asks AI for recommendations in your category, the sources it draws from shape the answer. If competitors have optimized their content for AI citation and you haven't, their brands get recommended while yours gets overlooked.
You have no strategy for earning AI citations
Traditional link building and SEO don't translate directly to AI citation. AI models evaluate content differently — looking for authority signals, factual density, and structured information. Without understanding what makes content citable by AI, optimization is guesswork.
Capabilities
Everything you need to master AI citations
Purpose-built for understanding and influencing the AI citation graph. See which sources AI trusts, why, and how to earn your place among them.
Citation Mapping
Visualize the complete citation graph for your category. See which sources AI models reference when answering questions about your industry, products, and competitors.
Source Authority Scoring
Get a quantified authority score that shows how trustworthy AI considers your content compared to competitors. Track your authority trajectory over time.
Competitor Source Analysis
See which sources power your competitors' AI visibility. Understand why certain brands get cited and others don't, and identify the content that gives them an edge.
Content Gap Identification
Discover topics and content types where AI cites competitors but not you. These are the gaps where creating authoritative content can earn you new citations.
Citation Trend Tracking
Monitor how citation patterns shift over time. See when new sources enter the citation graph, when existing sources lose authority, and how model updates change everything.
Recommendation Engine
Get AI-powered recommendations for making your content more citable. Specific, actionable suggestions based on what AI models actually value in your category.
Citation Graph
See the full map of what AI cites
Maya builds a visual citation graph showing every source AI models reference for your category. See which websites, articles, research papers, and data sources AI draws from when recommending products like yours. Understand the relationships between sources and identify the most influential nodes in the citation network.
- Interactive citation network visualization
- Source-to-recommendation path tracing
- Category-level citation landscape
- Influence node identification
Citation Graph
Authority Score
Know how authoritative AI considers your content
Every source in the AI citation graph has an authority weight — some sources heavily influence recommendations while others are barely referenced. Maya calculates your content's authority score across AI platforms and shows exactly how you compare to competitors. Track score changes as you optimize your content strategy.
- Per-domain authority scoring
- Competitor authority comparison
- Platform-by-platform authority breakdown
- Historical authority trend analysis
Authority Score
Citation Optimizer
Get specific actions to earn more citations
Knowing the citation landscape is only half the battle. Maya's Citation Optimizer analyzes high-authority sources in your category and reverse-engineers what makes them citable. You get a prioritized list of content improvements — from structural changes to topic coverage gaps — designed to make AI models cite your content more frequently.
- Prioritized content improvement actions
- Structural optimization suggestions
- Topic coverage gap analysis
- Citation-optimized content templates
Citation Optimizer
How It Works
From invisible to cited in three steps
Map your citation landscape
Enter your brand, category, and competitors. Maya queries AI platforms and builds a complete citation graph showing every source that influences recommendations in your space.
Analyze authority and gaps
See your authority score alongside competitors. Identify content gaps where competitors are cited and you're not, and understand what makes high-authority sources stand out.
Optimize and earn citations
Follow prioritized recommendations to improve your content. Track your authority score as it climbs and monitor new citations as AI models begin referencing your content.
Who It Helps
Built for teams that want AI to cite their content
Content Strategists
Build a content strategy informed by what AI actually cites. Prioritize topics and formats that earn citations, not just traditional search traffic.
PR Teams
Understand which publications and media sources AI models trust most. Focus earned media efforts on outlets that influence AI recommendations.
Link Building Teams
Go beyond traditional backlinks. Identify the sources AI values most and build relationships with the domains that drive AI citation authority.
Editorial Teams
Create content that AI models want to cite. Understand the depth, structure, and authority signals that make articles a go-to AI reference.
Frequently asked questions
What is AI source analysis?+
AI source analysis is the process of identifying and mapping which sources — websites, articles, research papers, datasets — AI models reference when generating responses. Unlike traditional SEO which focuses on search engine links, source analysis reveals the invisible citation graph that determines what AI recommends. Maya tracks these citations across ChatGPT, Claude, Gemini, Perplexity, and other platforms.
How does AI decide which sources to cite?+
AI models evaluate sources based on multiple factors: domain authority, factual accuracy, content depth, recency, topical relevance, and how widely a source is referenced across the web. Models trained on large datasets learn which sources are consistently reliable. Retrieval-augmented models also pull from real-time indexed sources. Maya analyzes these patterns so you can align your content with what AI values.
Can I see which specific pages get cited?+
Yes. Maya's citation mapping goes beyond domain-level analysis to show individual pages and articles that AI models reference. You can see exactly which of your pages (and competitors' pages) appear in AI citations, how frequently they're referenced, and for which types of queries. This page-level detail is essential for targeted content optimization.
How do I improve my citation rate?+
Maya's Citation Optimizer provides specific, prioritized recommendations based on analysis of high-authority sources in your category. Common improvements include adding structured data, increasing topical depth, publishing original research, improving factual density, and earning references from sources that AI already trusts. Each recommendation includes expected impact and implementation guidance.
Do different AI platforms cite different sources?+
Yes, significantly. ChatGPT, Claude, Gemini, and Perplexity each have different training data, retrieval mechanisms, and authority signals. A source that's highly cited by Perplexity (which uses real-time web retrieval) may not be referenced by ChatGPT (which relies more on training data). Maya tracks citation patterns per platform so you can optimize for each one independently.