Issue 4 — 28/04/2026

Anthropic's Claude Selects Citations Based on Entity Authority and Structured Data — Not Domain Authority Alone

Brain Buddy AI · 28/04/2026

According to a practitioner-facing guide updated April 2026, Claude's retrieval system weights entity authority, factual accuracy, structured data, and content clarity when selecting sources for inline citations. ClaudeBot configuration and llms.txt deployment are identified as direct optimisation levers. For GEO practitioners, this means domain-level authority scores are insufficient — content must also be entity-structured and machine-parseable to compete for Claude citations.

Google AI Mode Reaches 100 Million Monthly Active Users, Processes Over 1 Billion Queries

Miniloop AI Blog · 11/04/2026

Google AI Mode, launched May 2025, has scaled to over 75 million daily users and 100 million monthly active users as of February 2026, and processes over one billion queries. It operates as a separate ranking surface from both traditional Search and AI Overviews, using entity recognition and multimodal signals to generate synthesised answers with citations. Practitioners targeting B2B high-value traffic must treat AI Mode as a distinct optimisation channel with its own ranking logic.

Perplexity Surpasses One Billion Queries Per Month, Now Facing Publisher Legal Action

Byte Sized Tech Tips (Substack) · 27/04/2026

Commentary published 27 April 2026 notes that Perplexity now processes over one billion queries per month, up from 500–600 million reported in mid-2025, and is facing legal action from major publishers — an indicator of meaningful traffic displacement. The piece frames Perplexity's growth as a direct consequence of Google's business-driven decision to return links rather than answers. For ASO and GEO practitioners, this trajectory signals that Perplexity citation coverage is no longer secondary to Google optimisation for informational queries.

Perplexity Runs a Pre-Built Index — Real-Time Fetching Is a Myth That Changes Everything About Optimisation

AI + Automation Blog · 28/04/2026

An analysis of 818 Perplexity citations across 19,556 queries and eight industry verticals (attributed to Lee, 2026a — unconfirmed peer-reviewed status) establishes that PerplexityBot crawls and indexes content in advance; it does not perform live fetches at query time. The index is reported to be 3.3× fresher than Google's. Practitioners must ensure PerplexityBot is not blocked in robots.txt and that content is crawlable and indexable before a query is ever issued — reactive optimisation after the fact has no effect on citation eligibility.

AI Overviews Now Appear on 15–30% of Google Searches; Perplexity and ChatGPT Search Each Serve 100M+ Monthly Users

Over The Top SEO · 06/04/2026

A comparative analysis published April 2026 puts Google AI Overviews at approximately 15–20% of searches (a separate BrightEdge figure cited elsewhere places this at roughly 30%), Perplexity at over 500 million queries per month (now superseded by the one billion figure from Source 13), and ChatGPT Search at over 100 million monthly users. Each platform applies distinct citation logic: Google AI Overviews rewards established SERP authority, Perplexity rewards index freshness and direct-answer formatting, and ChatGPT Search performs closer to live web retrieval. Practitioners must map content format to platform retrieval architecture rather than applying a single GEO strategy across all three.

AI-Referred Traffic Converts at 6× the Rate of Non-Branded Organic — GEO Citation Volume Has Direct Revenue Implications

GrowthVibe · 28/04/2026

A guide updated April 2026 cites Webflow (2025) data showing AI-referred traffic converts at six times the rate of non-branded organic search traffic, and First Page Sage (January 2026) data placing ChatGPT at 60.7% market share among AI chatbots. Princeton and Allen Institute research is cited showing a 0.87 correlation between semantic completeness and AI citation rates (source methodology unconfirmed — treat as indicative). The conversion differential means even modest AI citation share can materially outperform high-volume organic rankings on revenue metrics.

GraphRAG: Combining Vector Search with Knowledge Graphs for Relationship-Aware Retrieval

ByteMentor AI editorial (practitioner article, not peer-reviewed arXiv) · https://www.bytementor.ai/blog/graphrag-next-evolution-retrieval-augmented-generation · 13/04/2026

Standard RAG retrieves semantically similar text chunks but fails on relational queries — it cannot resolve chains of dependency between entities. GraphRAG addresses this by layering a knowledge graph over vector search, enabling topology-aware queries such as provenance tracing and multi-hop relationship resolution. For GEO practitioners, this architecture is directly relevant: content structured around explicit entity relationships and typed citations is more likely to survive GraphRAG retrieval intact and be surfaced as a coherent, citable source rather than a fragmented chunk.

Hybrid RAG over Structured and Unstructured Data: Patterns for Production Systems

Zen van Riel (practitioner post, not peer-reviewed arXiv) · https://zenvanriel.com/ai-engineer-blog/rag-with-structured-data/ · 28/04/2026

This practitioner write-up documents production patterns for RAG pipelines that query both document corpora and relational databases simultaneously, routing queries by type without requiring users to specify the data source. The core finding is that vector similarity is insufficient for schema-aware, aggregation-based queries — hybrid routing logic is required. For ASO practitioners building knowledge bases intended to feed enterprise AI assistants, this highlights that structured metadata alongside prose content significantly improves retrieval precision.