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How We Study AI Visibility
AI search systems are building entity models of every organization. They crawl, cross-reference, and synthesize information from hundreds of sources to decide who to name in their responses. We study how those systems make citation decisions and help organizations understand where they stand.
Based on the three-signal diagnostic methodology developed by Mia Cheraghian, PhD.
AI Visibility Audit
A structured diagnostic that analyzes how AI systems currently interpret, reference, and cite your organization. We query ChatGPT, Perplexity, Google AI Overviews, and Copilot with the questions your audience actually asks, then evaluate the results against three diagnostic signals.
Can AI Find You?
Whether your content is structured, crawlable, and machine-readable. Evaluates schema markup, heading hierarchy, content architecture, and technical infrastructure.
Can AI Trust You?
Whether AI systems encounter consistent brand identity across every platform they cross-reference. Evaluates entity consistency across directories, LinkedIn, review sites, and your website.
Can AI Quote You?
Whether independent, trusted sources reference your organization in ways that give AI confidence to cite you. Evaluates third-party authority signals and external validation.
WHAT THE DIAGNOSTIC REVEALS
- How AI systems currently interpret your organization
- Where information gaps undermine AI confidence
- Which competitors AI cites instead of you, and why
- Signal-level assessment across all three dimensions
- Prioritized visibility recommendations
Most organizations score below 20% on AI visibility. Find out where you stand.
Request a Diagnostic →AI Visibility Strategy
A research-informed strategic analysis built on your diagnostic results. We map how AI systems currently construct knowledge about your category, identify where your organization is absent or misrepresented, and design the knowledge architecture needed to change how AI systems interpret you.
This is not a content calendar or an execution plan. It is a strategic assessment of how to structure your organization's information for AI systems that are deciding who to cite.
STRATEGIC ANALYSIS INCLUDES
- AI system interpretation analysis across major platforms
- Knowledge architecture recommendations for AI ingestion
- Entity alignment strategy across distributed sources
- Authority signal development and citation pathway analysis
- Generative search visibility strategy by platform behavior
IMPACT TIMELINE
Information architecture changes typically reflect in AI systems within 4-8 weeks. Authority signal development operates on a 3-6 month horizon and produces the most durable results.
AI Information Architecture
Traditional SEO focused on ranking individual web pages in a list of links. AI search systems work differently. They interpret organizations through distributed information across many sources: websites, directories, news articles, review platforms, social profiles, structured data, and more.
AI Information Architecture is the practice of structuring organizational knowledge so AI systems can correctly interpret, reference, and recommend an organization.
It considers how information is distributed, whether it is consistent, whether independent sources validate it, and whether it is technically readable by AI crawlers.
This is the layer between what your organization publishes and what AI systems understand about you. When done well, AI systems form an accurate, confident model. When neglected, AI either ignores you or misrepresents you.
PAST
Traditional SEO
Rank pages in a list of links
PRESENT
GEO
Optimize for AI-generated answers
NEXT
AI Information Architecture
Structure how AI systems understand your organization
// RESEARCH & INSIGHTS
Studying How Organizations Appear in AI Search
Miaren AI studies the emerging information layer between organizations and the AI systems that interpret them.
How AI assistants choose which organizations to recommend
Analyzing the citation selection process across ChatGPT, Perplexity, Google AI Overviews, and Copilot to understand what signals drive AI recommendations.
Visibility patterns across generative search platforms
Each AI system retrieves and synthesizes information differently. We study platform-specific citation behaviors and how they diverge from traditional search.
AI discoverability for nonprofits and mission-driven organizations
How organizations without large marketing budgets can structure their information for AI visibility through strategic information architecture.
Information architecture for generative search systems
How distributed organizational knowledge is structured to be correctly interpreted by AI systems that synthesize from multiple sources.
Executive Intelligence Briefings
Structured briefings for leadership and technical teams who need to understand how AI search systems evaluate and cite sources. Grounded in the three-signal methodology and current research on generative search behavior.
DESIGNED FOR
BRIEFING COVERS
- How AI search systems construct knowledge and select citations
- The three diagnostic signals that determine AI visibility
- How to evaluate your organization's current AI presence
- Strategic considerations for AI information architecture
- Ongoing assessment criteria and monitoring frameworks
Not sure where to start?
Start with a conversation. We'll help you understand what AI search systems currently say about your organization.