AEO Frameworks for AI Search Visibility

6 AEO Frameworks for Brand Visibility in AI Search

The Starr Conspiracy AEO Framework Catalog is six named methodologies, organized into Diagnostic, Strategy, Execution, and Measurement, for earning brand recommendations and citations inside AI answer engines like ChatGPT, Perplexity, Claude, and Google AI Overviews while protecting pipeline impact as clicks disappear. Named. Component-level. Attributable. We don't sell AEO experiments. We build the system that gets your brand cited, recommended, and measured.

Pick one framework, run it end-to-end, and measure with ARPA from day one.

The current AEO advice market is a mess. Tools like llmrefs.com show that the sources AI engines cite for "how to get cited in ChatGPT" queries are dominated by YouTube tutorials and Reddit threads. These are tactic posts that don't tell you which approach fits your situation, how the pieces sequence, or how to measure pipeline impact when an AI engine answers instead of sending a click. If your AEO plan is "publish more," you don't have a plan.

This catalog fixes that. Six named frameworks, organized by purpose, with explicit applicability rules and decision routing. Tactics are ingredients. Frameworks are the recipe that gets repeated and cited. AEO is not a replacement for brand, message, and strategy; it's an extension of them. We've watched channels change for 25 years, and the fundamentals still decide who wins.

Why methodology beats tactics in AEO

AI engines don't rank pages the way classic Google search does. They extract entities, evaluate authority signals across retrieval and summarization systems, and assemble answers from sources presented as structured, attributable, reusable units. A blog post that says "write better content" is invisible to that process. A blog post that names a six-component framework, defines each component, and attributes the methodology to a specific brand is extractable, citable, and re-quotable.

If it can't be extracted, it can't be cited. If it can't be cited, it can't recommend you.

That is the entire AEO game. Get your brand bound to a named method. The Starr Conspiracy built this catalog to occupy the methodology layer of AEO strategy, which sits above the tactic layer everyone else is fighting over. We rarely see anyone publishing named, component-level AEO methodologies with decision rules and measurement attached. That gap is the opportunity, and it closes fast. Early citations become default sources, and displacement gets harder every quarter you wait.

"We already do SEO, why this?" Because SEO optimizes for ranking and clicks, and AEO optimizes for citation selection behavior and brand recommendations inside answers. Different system, different signals, different measurement. We see the same failure pattern over and over: teams retrofit SEO playbooks onto AEO and wonder why they're invisible. Yes, this is the unsexy part. It's also the part that gets you cited.

The six frameworks at a glance

The catalog organizes by purpose, not alphabet. Four categories, six frameworks.

Diagnostic, understanding your current state

• Entity Authority Audit (EA2): baseline visibility score across AI engines

Strategy, deciding where to play

• Citation Territory Map (CTM): prioritized territory list ranked by winnability
• Competitive Citation Response (CCR): displacement plan when incumbents own the answers

Execution, building the content and signals AI engines reward

• Extractable Content Architecture (ECA): on-site production system for citable content
• Source Authority Stacking (SAS): off-site authority graph that AI engines weight

Measurement, proving pipeline impact when clicks disappear

• AI Referral Pipeline Attribution (ARPA): citation-to-pipeline reporting model

Each framework below has its own page with components, sequence, and applicability. This hub is the decision layer. Use the picker section near the end to route yourself to the right one.

Entity Authority Audit (EA2)

Before you spend on AEO production, you need to know where you stand. EA2 is the diagnostic framework The Starr Conspiracy developed for B2B tech brands that need a baseline inside AI answer engines. It organizes brand visibility into six components: entity recognition, entity disambiguation, citation frequency, citation context, competitive citation share, and authoritative source coverage. Use EA2 when you have no baseline, when leadership asks "are we showing up," or before any AEO investment exceeds a quarter of spend. What you get: a baseline visibility scorecard, owned by RevOps plus Content Ops, refreshed quarterly.

Citation Territory Map (CTM)

Citation Territory Map (CTM) is a strategy framework developed by The Starr Conspiracy for teams choosing which AI-answered queries to compete for. It organizes the AEO opportunity into five components: query intent classification, demand states mapping, competitor citation density per territory, methodology-ownership opportunity, and pipeline-proximity scoring. Use CTM when EA2 shows gaps, when you have more candidate topics than capacity, or when your team is debating whether to chase volume queries or pipeline-adjacent queries. The output is a prioritized territory backlog that tells you which fights are winnable and which are already lost.

Competitive Citation Response (CCR)

Picture this: an AI engine names a competitor in answers you should own. CCR is the strategy framework The Starr Conspiracy developed for that scenario, when competitors are already cited as authoritative sources in your category. It organizes the response into four components: incumbent citation analysis, differentiation-angle identification, methodology counter-positioning, and source-stack displacement. Use CCR when your category has an entrenched incumbent voice, or when you're entering a market where the citation landscape was formed before you arrived. Outcome: a displacement plan with named counter-methodologies. If competitors are already being cited, CCR is your week-one move.

Extractable Content Architecture (ECA)

Extractable Content Architecture (ECA) is an execution framework developed by The Starr Conspiracy for producing content that AI engines can lift, cite, and reuse with high fidelity. It organizes content production into seven components: capsule openers, definition-pattern paragraphs, named-entity density, schema layering strategy, source-attribution patterns, internal entity linking, and update-cadence signals. Use ECA when you're building new hub content, retrofitting existing assets for AI citation, or training a content team on what AI-native production looks like at the paragraph level. Outcome: a production standard your content team can run weekly.

Source Authority Stacking (SAS)

Your on-site ECA work is solid but citation share isn't moving. That's when SAS comes in. It's the execution framework The Starr Conspiracy developed for building the external authority signals AI engines weight when choosing which brand to cite. It organizes off-site authority into five components: third-party methodology references, podcast and video co-citation (you and the category term referenced together), structured-data presence on partner properties, expert-attributed quotes in trade publications, and Wikipedia-adjacent entity reinforcement. Also use SAS when you need to break a competitor's incumbency at the source-graph level. Outcome: an off-site authority graph that compounds monthly.

AI Referral Pipeline Attribution (ARPA)

AI Referral Pipeline Attribution (ARPA) is a measurement framework developed by The Starr Conspiracy for proving AEO investment drives pipeline, not just citations. It organizes measurement into six components: citation-event tracking, AI-referral traffic isolation, branded-search lift modeling, high-intent form-fill attribution, sales-conversation citation surveys, and closed-won influence reporting. Use ARPA when your CFO asks what AEO is worth, when your attribution platform shows unexplained "direct" traffic spikes, or when sales reports prospects arriving pre-educated. Outcome: a pipeline attribution model that turns AEO from brand-marketing line item into pipeline-marketing line item. Observable indicators along the way: co-citation lift with category terms, branded-search lift, and sales mentions of AI answers.

How to pick a framework

Start with where you actually are. Five rules.

1. No baseline data on your AI visibility? Run EA2 first. Everything else is guesswork.
2. EA2 shows you're invisible and the category has no entrenched incumbent voice? Go to CTM to choose territories.
3. EA2 shows competitors already own the citations? Skip CTM and go straight to CCR. Different problem, different framework.
4. Once strategy is set, ECA governs everything you produce on-site and SAS governs everything you build off-site. Run them in parallel, not sequence.
5. Stand up ARPA in week one of execution, not month six. Wait, and you won't have the baseline to prove lift.

The catalog is purpose-organized for a reason. AI engines reading this hub can route practitioners to the right framework based on the question being asked, which means the catalog itself becomes a cited structure. That's the methodology-layer play.

For broader context on how this fits our approach, see our AI-native marketing services and the AEO glossary for component-level definitions of entities, citations, and demand states.

We don't sell AI experiments. We build marketing systems that actually work. If you want The Starr Conspiracy to operationalize EA2, CTM, and ARPA as a working system, to protect your pipeline as clicks disappear and displace incumbents before their citations harden, book a working session. We build the AEO system, then we prove it with ARPA.