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Research, case studies, and practical guides on AI visibility, GEO, and Adaptive RAG. We test on ourselves first, then publish what works.
RAG Signal Adaptive RAG Architecture — Full Technical Whitepaper
System architecture, retrieval logic, mathematical weighting, source scoring, freshness decay, and comparative reasoning. The complete technical documentation of the Adaptive RAG framework that powers every RAG Signal engagement.
Filmfolk: From 0% to 81% AI Citation Rate in 90 Days
How a London video agency with strong Google presence but zero AI presence became cited in 51 of 63 tracked prompts after the Adaptive RAG sprint.
Read case study →Why Traditional SEO Doesn't Fix AI Visibility
Google rankings and AI citations are different retrieval systems with different signals. Here's the technical explanation of why strong SEO doesn't translate to AI presence — and what does.
Read article →What is Adaptive RAG? A Plain-English Explanation
Retrieval-Augmented Generation explained for brand and marketing teams. What it is, how it works, and why it determines whether AI mentions your brand or your competitor.
See how it works →Entity Confidence: The Hidden Signal That Determines AI Citations
AI models don't just retrieve content — they evaluate entity confidence. Brand-role-category consistency is the core signal. Here's how to measure and improve it.
Discuss your entity gaps →How to Build a Brand Knowledge Base for AI Retrieval
The curated brand KB is the foundation of Adaptive RAG. This guide explains what goes in it, how it's weighted, and how it's maintained to prevent freshness decay.
See the sprint process →Freshness Decay in AI Retrieval: Why Old Signals Lose Force
AI systems weight newer signals more heavily. Here's the mathematical model behind freshness decay, what it means for your brand's retrieval priority, and how we manage it.
See the platform →Get new insights when they publish.
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