Insights & Analysis
Blog
Research-backed insights on AI citations, GEO strategy, and the evolving landscape of generative search.
How ChatGPT Decides What to Recommend: Inside the Recommendation Engine
ChatGPT processes 2.5 billion daily prompts, many of which are recommendation queries. The AI draws on three knowledge layers — parametric knowledge, RAG retrieval, and system instructions — to decide which brands to mention. Seven factors drive recommendations: referring domain count (strongest), authoritative list presence (41%), awards (18%), reviews (16%), brand frequency, recency, and Wikipedia presence.
March 5, 2026
Perplexity Citations: How to Get Your Content Referenced
Perplexity's citation-first architecture makes it the most transparent AI search platform. Every response includes numbered, clickable source links. The 7-step optimization guide covers SEO fundamentals, content structure, data points, direct answers, freshness, crawler access, and referral tracking. Perplexity citations correlate strongly with search rankings, making it the ideal GEO testing ground.
February 28, 2026
AI Citation Metrics: How to Measure Your AI Visibility
The AI Citation Score framework addresses the measurement gap in AI visibility with 5 components: citation frequency (30% weight), accuracy (25%), prominence (20%), cross-platform consistency (15%), and sentiment (10%). Each scored 0-100 to produce a composite score. Industry benchmarks range from 80-100 (category leader) to 0-19 (invisible to AI).
February 20, 2026
The Science of AI Recommendations: What Academic Research Tells Us
Academic research reveals four key themes in LLM recommendation behavior: factual recall correlates with source frequency and consistency, popularity bias creates self-reinforcing citation advantages, RAG systems favor structured and direct-answering content, and systematic citation gaps affect non-English sources, SMBs, new entrants, and niche specialists.
February 12, 2026
Industry AI Citation Report: Who Gets Recommended and Why
Cross-industry analysis of 1,200 queries across ChatGPT, Perplexity, and Gemini reveals AI citations follow a severe power law: top 3 brands capture 65-80% of mentions per category. Cited brands have 3.2x more referring domains, 89% appear on authoritative lists, and 72% have Wikipedia articles. Industry-specific dynamics vary significantly across SaaS, healthcare, professional services, e-commerce, education, and travel.
February 1, 2026
llms.txt and robots.txt for AI: Technical Guide to AI Crawler Management
Two files control AI crawler access: robots.txt tells crawlers where they can go, llms.txt tells AI models what your site is about. This guide covers GPTBot (OpenAI), PerplexityBot, ClaudeBot (Anthropic), and Google-Extended configuration, the llms.txt specification with 5 key fields, Schema.org structured data for AI, and testing procedures.
January 18, 2026
From SEO to GEO: The Complete Migration Guide
The SEO-to-GEO migration is an expansion, not a replacement. Core SEO skills — E-E-A-T, technical optimization, link building, content structure — transfer directly to GEO. The 5-step framework covers AI visibility audit, technical foundation (robots.txt, llms.txt, Schema.org), content optimization for citation extraction, authority building on authoritative lists (41% of AI recommendations), and ongoing measurement.
January 5, 2026
Stay ahead of AI search changes
Get research updates, citation insights, and tool announcements.