Understand how machines discover the web.
The open intelligence layer for AI discovery: a living, evidence-backed record of how machines discover, retrieve, cite, recommend, and act on information.
What changed, and why it matters.
Every update is placed inside a persistent historical, semantic and evidentiary record instead of disappearing into a news archive.
Perplexity Q2D-Web maps the retrieval stage behind agentic search
Nearly 70,000 machine-reformulated queries show why the query a user types is not necessarily the query your content competes for.
Cloudflare’s new Search / Agent / Training crawler defaults take effect
Cloudflare’s announced defaults take effect for new domains: Training and Agent traffic is blocked by default on ad-supported pages while Search remains allowed; multi-purpose crawlers are governed by their most restrictive applicable purpose.
Perplexity introduces Q2D-Web for large-scale retrieval evaluation in agentic RAG
Perplexity released Q2D-Web, a production-shaped retrieval benchmark built around 190 million web documents and 69,721 agent-reformulated queries across 10 languages.
Cloudflare expands BotBase for bot and agent transparency
Cloudflare expanded BotBase so bot and agent operators can identify themselves and document what their automated systems do.
Cloudflare separates AI traffic into Search, Agent, and Training
Cloudflare introduced a crawler-purpose taxonomy separating Search, Agent and Training traffic and announced new default controls.
Google publishes official guidance for generative AI Search visibility
Google Search Central published a guide explaining generative AI Search, including RAG, query fan-out, and its position that core SEO practices remain relevant.
AI visibility did not begin with GEO.
Visibility OS follows the longer story: how documents became machine-readable, how search learned entities, how answers replaced result lists, and how agents now fan out queries across the web.
Semantic web
Structured data made meaning more explicit to machines.
Entity understanding
Search moved from strings toward entities and relationships.
Answer extraction
Featured answers made being selected different from simply ranking.
Retrieval-grounded generation
RAG connected generative models to external information.
Generative search
AI systems began synthesizing answers across multiple sources.
Query fan-out & agents
A single prompt can trigger many machine-generated searches and actions.
Explore by question, not just category.
The interface should match how people actually investigate AI discovery: by asking what changed, what is proven, and what remains uncertain.
Living reference pages, not disposable posts.
AEO, GEO and LLMO sit inside a broader graph of retrieval, source selection, citations, discoverability, measurement and agentic behavior.
Generative Engine Optimization (GEO)
Optimization for visibility, citation, or inclusion in generative search and answer engines.
TopicAnswer Engine Optimization (AEO)
Optimization for systems that return direct answers rather than only ranked links.
TopicLarge Language Model Optimization (LLMO)
Optimization aimed at how LLM-based systems understand, retrieve, cite, or recommend entities and content.
TopicQuery Fan-Out
Decomposing a user question into multiple related searches or subqueries.
TopicAI Citations
Attribution, linking, and source selection in AI-generated answers.
TopicRetrieval
Finding candidate information or documents for an AI response.
TopicAI Discoverability
Whether AI systems can find and retrieve information from a source.
TopicAI Visibility Measurement
Methods and metrics for measuring mentions, citations, share of voice, and discoverability.
Facts, interpretation and uncertainty stay separate.
Each claim is attached to source quality and evidence status. Visibility OS is designed to preserve the evidence trail, not flatten every industry claim into advice.
What the industry still does not know.
Uncertainty is part of the dataset. We track unresolved terminology, causal claims and measurement problems instead of pretending consensus exists.
What is the earliest verifiable published use of the term “Answer Engine Optimization” (AEO)?
The practice predates the terminology, and current web sources frequently repeat unattributed origin claims.
What is the earliest verifiable use of “Large Language Model Optimization” / LLMO for web visibility?
The acronym is used inconsistently and overlaps with unrelated ML optimization meanings.
Does llms.txt materially affect inclusion, citation frequency, or retrieval in major AI search systems?
Adoption does not establish causal visibility impact.
Built to be read by people and machines.
Semantic HTML, explicit dates, controlled vocabulary, source attribution and stable canonical pages make each record understandable even when the visual layer is stripped away.
Discovery can be automated, but publication is reviewed. Important records retain primary sources, evidence notes, relationships and revision history.
Original sources and research records are also archived separately so the intelligence corpus is not dependent on one database or hosting provider.