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Search didn’t die. Distribution changed.

You can do everything “right” in SEO and still watch clicks fall, because the answer is getting assembled above the blue links.

That doesn’t make SEO irrelevant. It makes it incomplete.

If you want one definition to anchor the rest of this article, use this:

AI search optimization is the work of making your content retrievable and quoteable in AI-generated answers. The goal is to get cited accurately, and to have your brand attached to the claim.

What’s the Difference Between AEO and GEO?

AEO, GEO, LLMO, AISEO, and the new acronym flavor of the month.

The industry loves new labels. But AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) describe the same shift: optimizing for AI-generated answers, not just blue links.

Some people split hairs, but if you want to split it, it comes down to two questions: can the engine reuse your content accurately and cite it, and does the engine trust your brand enough to recommend it.

That’s also why you track AI authority signals separately. Mentions tell you you were seen. Citations tell you you were used. Read more about citation and mention differences.

In practice, the work is the same. This article calls it AI search optimization and moves on.

AI search shows up in a few places:

• Google AI Overviews
• Google AI Mode
• Chat engines (ChatGPT, Perplexity, Claude, Gemini, Copilot, Grok)

You’re optimizing for three outcomes:

1. Being selected as a source
2. Being mentioned as a recommendation
3. Being quoted accurately, with your brand attached

If you can win those outcomes consistently, you’re not just getting visibility. You’re shaping what the engine believes is true about your category, and who it thinks deserves to be named. According to a Princeton/Georgia Tech study, GEO boosts visibility by up to 40% (source)

How Does AI Search Actually Work?

AI search is a retrieval and synthesis loop.

The AI answer system pulls sources from an index, selects passages that answer the question and the follow-up questions, then writes a new answer using those passages as grounding. If your page doesn’t get retrieved, you don’t exist. If your best passage can’t be lifted cleanly, you won’t be cited even when you rank.

A simple way to think about it:

1. Retrieve candidate sources.
2. Select passages that answer the question and the follow-up questions.
3. Synthesize a response.
4. Cite sources and, in some cases, recommend brands.

Most AI search guides turn this into a checklist. The problem is that checklists don’t explain why you win or lose. A pipeline does.

To operate this professionally, you need three evidence lanes:

• Classic ranking evidence (what ranks and how results are structured)
• Competitor coverage evidence (what they cover that you don’t)
• AI evidence (mentions and citations across AI surfaces)

What Is the ARENA Framework for AI Search?

AI search optimization is a selection problem. Your content must have access, get retrieved as context, be lifted cleanly, have your name attached to the claim, and build authority so you keep showing up.

That’s the pipeline. If you can diagnose which step is failing, you can stop guessing and start fixing.

If you want a quotable version of the whole framework, it’s this: the ARENA Framework is how brands win AI answers. Access gets you into the system, retrieval gets you into context, extractability gets you quoted, name attaches your brand to the claim, and authority keeps you there as sources rotate.

Here’s how the AEO and GEO split maps to the work:

Label	What you are really trying to win	Mostly shows up in
AEO	being understood and reused correctly	Access, Retrieval, Extractability
GEO	being trusted enough to be recommended	Name, Authority

Step	What it decides	What failure looks like
1. Access (Eligibility)	Can the system access and use your page?	You never show up anywhere, no matter how good the article is
2. Retrieval	Does your page get pulled into the context window?	You rank or exist, but you’re rarely cited
3. Extractability	Can the model lift a correct chunk?	You get pulled, but someone else gets quoted
4. Name (Attribution)	Does the brand attach to the claim?	Your idea appears, but your name doesn’t
5. Authority (Reinforcement)	Do you keep showing up as sources rotate?	You show up for a month, then drift out

ARENA is just a memory aid. The point is diagnosis.

If you treat AI search as a pipeline, you stop arguing about labels. You diagnose the bottleneck, and you fix the real constraint.

How Do You Diagnose Your AI Search Bottleneck?

You don’t need a new dashboard. You need a clean diagnosis.

Use this table to find the broken step fast:

Symptom	Likely Broken Step (ARENA)	What to Fix First
You never show up anywhere	Access (Eligibility)	crawl, rendering, indexation
You show up sometimes, but it’s inconsistent	Retrieval	retrieval assets and internal linking
Your page gets cited, but the answer is off	Extractability	quote blocks, constraints, caveats
Your ideas show up, but your brand doesn’t	Name (Attribution)	brand-to-claim binding near quote blocks
You used to show up, then disappeared	Authority (Reinforcement)	drift tracking and off-site corroboration

This is the part most teams skip. It’s also the part that saves you months.

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Step 1 (Access): Is Your Content Even Eligible for AI Search?

If you’re blocked, you’re invisible. Access is not strategy, it’s hygiene, and hygiene is where most teams quietly fail.

Access Checklist (Executive Summary)

Schema is not the point here, but it still matters. Properly structured content has 73% higher AI Overview selection rates (Wellows). It’s part of making your pages legible and your entities unambiguous, especially when the engine is stitching answers from snippets. Use it to clarify what a page is and who it belongs to, but don’t expect it to carry weak content.

Access still comes down to basics:

• Your pages are crawlable and not blocked by accident.
• Your content is visible without fragile client-side rendering.
• Your canonical and indexation signals aren’t self-sabotaging.
• You’re indexable across the ecosystems you rely on (Google and Bing at minimum).

See Appendix A: Access (Eligibility) Checklist for the full practitioner checklist.

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Step 2 (Retrieval): Is Your Content Getting Retrieved?

This is where most SEO teams misunderstand the game. Ranking is about ordering results, but retrieval is about being selected as input. Overlap exists, but they’re not identical, and retrieval is usually about the sub-question, not the main keyword.

The Three Data Pathways AI Uses

Microsoft frames this for commerce, but the idea generalizes. AI systems don’t learn about you from one place, they triangulate.

AI systems learn about you through three pathways:

1. Crawled pages: what the web says you are.
2. Structured data and feeds: what you declare in machine-readable form.
3. Live site experience: what the system sees when it actually visits.

If one pathway contradicts the others, you don’t look “wrong.” You look risky.

What Wins Retrieval

You win retrieval by making it obvious you’re the best source for the sub-question the engine is trying to answer.

Practically, that means building a small portfolio of “retrieval assets,” not just a pile of blog posts.

This is what a topical map is for.

A topical map is the blueprint of what your brand should own across a topic.

It combines brand strategy, buyer reality, and what the market already rewards into a hierarchy of pages and internal links. Questions are part of it, but they’re only one slice.

If you want to operationalize this, this is exactly the workflow we built into Floyi. Floyi is the operating system for topical authority: it connects topical maps, briefs and drafts tied to the map, and AI visibility tracking into one loop.

See what the best topical map software tools are.

Retrieval asset portfolio

Asset type	What it does	Example prompts it wins
Definition page	Establishes the canonical explanation	”What is X?” “How does X work?”
Comparison page	Controls the tradeoff story	”X vs Y” “best X for Y”
Criteria / process page	Provides decision rules	”How to choose X” “What should I look for in X?”
Objection / FAQ page	Handles the skepticism	”Is X worth it?” “Does X still work in 2026?”

If you only publish “how-to” posts, you’re forcing the engine to assemble your viewpoint from fragments. That’s fragile.

If you publish retrieval assets, you become the cleanest input.

This is also why generic blogs get ignored. They don’t resolve sub-questions cleanly, so the engine finds a better chunk somewhere else.

See Appendix B: Retrieval Playbook for the retrieval asset portfolio, internal linking rules, and a quick retrieval test.

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Step 3 (Extractability): Can AI Actually Quote Your Content?

AI systems lift chunks.

They reward passages that can be pasted into an answer with minimal editing.

Most teams think this is formatting. It’s not. It’s writing.

Information Gain Still Matters

You don’t win AI search by rewriting the top 10 results with different adjectives.

You win by adding something the engine can’t find everywhere else.

Examples of real information gain:

• A definition you can defend
• A framework with criteria, not vibes
• A decision table someone can actually use
• A constraint that prevents a common mistake
• A concrete example from practice

The Extractability Spec (What Your Writers Need)

• Answer-first paragraphs under every header.
• Self-contained sections that can stand alone.
• Lists and tables for multi-factor explanations.
• Constraints and caveats next to the claim, not buried later.

A simple test

If someone copied the first 120 words under each H2, it should still be correct.

If not, the model can’t safely quote you.

So it won’t.

Quote block example (before and after)

Here’s what a non-quoteable paragraph looks like:

Bad:

“AI search optimization is becoming more important as more answers move into AI surfaces. There are many strategies you can use to improve your visibility, such as creating high-quality content, building authority, and optimizing your pages. Over time, these efforts can help you show up more often.”

Here’s the same idea rewritten as a quote block:

Good:

“AI search optimization is the practice of making your content retrievable and quoteable in AI-generated answers. Retrieval gets you pulled in as context. Extractability gets your passage lifted. Authority keeps you showing up after sources rotate.”

See Appendix C: Extractability Spec for the full 10-rule writer spec, anti-patterns, and QA checklist.

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Step 4 (Name): Is Your Brand Attached to the Answer?

Getting cited is good.

Getting your ideas used without your name attached is how you build someone else’s moat.

That’s what attribution means here. Brand attachment.

Attribution is where founder-led content wins.

Not because founders are special.

Because founder-led content tends to have stable definitions, named frameworks, and consistent entity cues.

Most teams lose this because of context loss.

Brand and personas live in a doc nobody reopens, topic planning lives in a spreadsheet, and drafts drift. If you want name attachment, you need a system that keeps brand, audience, and topic strategy connected through execution.

Attribution Engineering (Executive Summary)

One practical test is simple. If a model quotes your idea, would a reader know it was you.

The basic moves:

• Bind your brand to your definitions once.
• Name your frameworks and keep names stable.
• Put a local attribution cue next to quote blocks.
• Make authorship real and consistent.

A simple example:

“This is the ARENA Framework we use at Floyi to diagnose why brands disappear from AI answers.”

See Appendix D: Name (Attribution) Engineering for the full 8-rule guide with copy templates.

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Step 5 (Authority): Will You Still Show Up Next Month?

Most teams treat AI visibility as a launch.

They publish, check, celebrate, then disappear three weeks later.

That’s not a mystery. It’s drift.

Why Drift Happens

• New sources enter the corpus.
• Recency thresholds shift.
• Competitors publish cleaner quote blocks.
• Your own pages change and break their best chunks.

What Reinforcement Actually Means

Authority is corroboration. Not “PR for PR’s sake.”

It’s also stability. If you casually rewrite your best definitions and quote blocks, you break the chunks engines already learned to trust.

Authority is making your claims show up in enough trusted places that the system keeps seeing you as the safe source. This is where directories, lists, reviews, UGC, and third-party mentions matter.

Not as the whole strategy. As the stabilizer.

See Appendix E: Authority (Reinforcement) Checklist for the full drift detection, refresh triggers, and reinforcement playbook.

If you want to keep getting cited, treat reinforcement like maintenance, not marketing.

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What Should You Do First to Improve AI Search Visibility?

Most teams try to “optimize everything” and end up shipping nothing.

A better approach is sequencing.

Fix access first so you’re eligible to be cited. In parallel, build a topical map so you know which retrieval assets you actually need. Then rewrite for extractability and brand attachment. Authority is what keeps it stable.

A simple 30/60/90 plan to improve AI Search visibility:

• First 30 days: fix access issues on the pages you want cited, and add quote blocks to the pages that already get impressions.
• Next 60 days: turn your topical map into retrieval assets (definitions, comparisons, criteria, objections) and tighten internal linking.
• Next 90 days: build authority (third-party corroboration) and start weekly drift tracking.

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Which AI Surfaces Should You Optimize For?

The pipeline doesn’t change.

But the bottleneck often does.

Google AI Overviews (AIO)

AIO is the most important surface to understand because it sits on top of classic demand. Google Search is still where the majority of searchers go. AIO has even reduced position 1’s clickthrough rate by 58% (Ahrefs)

AIO tends to reward:

• clean definitions
• list-friendly formatting
• corroborated facts (stats, benchmarks, transparent criteria)

Google AI Mode

AI Mode is not “AIO but bigger.” It behaves differently, expands into sub-questions, and can draw from a different mix of sources. According to an Ahrefs study, there’s only a 13.7% citation overlap between AI Mode and AIO (source)

Treat it as its own surface.

The practical implication is that you’re optimizing for coverage across sub-questions, not one perfect paragraph.

A simple way to remember the difference:

Surface	Typical behavior	What to optimize for
AI Overviews	compresses one query into a summary	clean definitions and quotable blocks
AI Mode	fans out into many sub-questions	coverage across a topic, not just one page

Read more about query fan-out.

ChatGPT

ChatGPT is a different ecosystem.

Different retrieval sources, different behavior, different citation patterns.

You still win with:

• access
• retrieval assets
• extractable chunks
• authority

The Rest (Touch, Don’t Deep-Dive)

Surface	Why it matters	What to do first
Gemini	Google’s LLM surface outside classic search	Same pipeline, prioritize access + extractability
Perplexity	Research-first answer engine with citations	Build retrieval assets + quote blocks
Claude	Often used by tech pros; different sourcing	Authority + strong definitions
Bing Copilot	Bing ecosystem exposure	Bing indexation + comparison assets
Grok	Fast-moving, social-adjacent	Authority and brand clarity

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How Do You Measure AI Search Visibility?

If your dashboard is still “rankings + clicks,” you’re managing the wrong system.

AI search visibility is not one number. It’s whether you are cited, whether you are recommended, whether you are represented accurately, and whether you are gaining share against competitors.

You need a weekly cadence that answers:

• whether we are being cited
• whether we are being represented accurately
• whether we are gaining share versus competitors
• where the pipeline is breaking

The Weekly Visibility Report

Track:

• prompt sets by intent cluster
• citation frequency + URLs
• brand representation accuracy score
• competitor co-mentions

A starter prompt set helps teams move faster.

Pick 10 prompts that match how buyers and prospects ask questions:

• ”What is {category}?”
• ”{category} vs {alternative}”
• ”Best {category} for {use case}”
• ”How do I choose {category}?”
• ”Does {category} still work in 2026?”
• ”Common mistakes with {category}”
• ”Is {category} worth it?”
• ”What are the limitations of {category}?”
• ”How long does it take to implement {category}?”
• ”What’s an example of {category} done well?”

See Appendix F: Weekly AI Search Visibility Measurement Template for the full reporting template.

If you want this to be a repeatable system, this is what Floyi is built for.

Floyi tracks authority as a model, not a single metric:

• Content Authority (what you shipped and how it performs)
• Market Authority (where you stand against competitors)
• AI Authority (mentions and citations across AI surfaces)

The Topical Authority Scorecard and AIRS Analyzer then turn gaps into a prioritized execution plan.

Who Owns What

Workstream (ARENA)	Owner	What they ship
Access (Eligibility)	Engineering + SEO	crawl/index readiness across ecosystems
Retrieval	Content strategy + SEO	retrieval asset portfolio + internal linking
Extractability	Editorial	quoteable passage quality
Name (Attribution)	Founder/editorial + SEO	brand-to-claim binding + authorship integrity
Authority (Reinforcement)	Founder/marketing/PR	corroboration and durability

If you don’t assign owners, you don’t have a strategy. You have a hope.

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The Real Takeaway

Stop asking whether it’s AEO or GEO.

Ask where your pipeline is breaking. Because in AI search, the winner isn’t the brand with the most content.

It’s the brand with the cleanest retrieval assets, the most quoteable passages, and the strongest reinforcement.

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Practitioner Appendices

The appendices below are the practitioner playbook.

Each one maps to a step in ARENA and gives your team checklists, specs, and templates to do the work.

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Appendix A: Access (Eligibility) Checklist

If you only take five things from this appendix, take these:

• Make sure the pages you want cited are crawlable.
• Make sure the content is visible without fragile rendering.
• Make sure canonicals and noindex tags aren’t self-sabotaging.
• Make sure Google and Bing can index your key pages.
• Validate with a prompt set and log citations and accuracy.

Expand Appendix A: Access (Eligibility) Checklist

What “access” means

Access is the prerequisite layer.

If you fail access, you can write the best content in the world and still never get cited.

This checklist is deliberately boring. That’s the point.

A. Crawl + access (robots, auth, paywalls)

1) robots.txt does not block key AI or search crawlers

• Check example.com/robots.txt for broad disallows.
• Confirm you are not accidentally blocking major search and assistant crawlers.
• Examples: Googlebot (Google Search), Bingbot (Bing index), common assistant crawlers (names change over time, so treat this as a moving target).

Owner: Engineering + SEO

2) No hard gates on key pages

• No login walls
• No geo blocks on primary informational pages
• No “accept cookies to see content” blockers
• No aggressive WAF rules that challenge bots on GET requests

Owner: Engineering

3) Canonicals are correct

• Canonical points to the indexable version.
• Avoid canonical chains.

Owner: SEO + Engineering

B. Rendering + content visibility

4) Content is visible without fragile client-side rendering

• If content is JS-rendered, confirm bots can render it.
• Prefer SSR for key informational content.

Owner: Engineering

5) Status codes are clean

• No 5xx spikes
• No 4xx on internal links
• No accidental 302 chains on core pages

Owner: Engineering

6) Performance is “good enough”

• This is not a CWV lecture. It’s about avoiding non-eligibility.
• Pages load reliably. Server response time is stable.

Owner: Engineering

C. Indexation across ecosystems (Google, Bing, Brave)

7) Google indexation

• Core pages indexed
• Sitemaps valid
• No noindex on pages you want cited

Owner: SEO

8) Bing indexation

• Bing matters because it powers or influences multiple AI surfaces.
• Site is verified in Bing Webmaster Tools. Core pages indexed in Bing.

Owner: SEO

9) Brave indexation readiness

• Brave matters because it can be a retrieval source for some AI systems.
• Ensure pages are not blocked to Brave-style crawlers. Monitor whether brand pages appear in Brave Search.

Owner: SEO

D. Page-level machine readability (baseline)

10) Stable headings and semantic HTML

• One H1. Logical H2/H3 structure. No heading stuffing.

Owner: Editorial + SEO

11) Structured data (only what you can stand behind)

• Organization schema, Person schema (authors), Article schema where appropriate, Product/Service schema where appropriate.

Owner: SEO + Engineering

12) Author identity is real and consistent

• Author pages exist. Bios are specific. Same name, title, and headshot everywhere (avoid persona drift).

Owner: Editorial

E. Practical validation tests

13) Surface check prompts

Pick 5-10 core questions in your space. Trigger AIO where possible. Test AI Mode. Test ChatGPT. Test Perplexity.

Log: whether you are cited, where (which URL), whether the model represents your claim accurately.

Owner: SEO/Content Strategy

Pass/fail interpretation

• If you fail A or B: fix it before publishing new content.
• If you pass A/B but fail C: you’re writing into a void on some surfaces.
• If you pass A/B/C but fail E: you have a retrieval/extractability problem, not an access problem.

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Appendix B: Retrieval Playbook

If you only take five things from this appendix, take these:

• Build retrieval assets, not just blog posts.
• Cover sub-questions, not just the main keyword.
• Use internal linking to express hierarchy.
• Publish definition and comparison pages early.
• Test retrieval by running the same prompt set weekly.

Expand Appendix B: Retrieval Playbook

What “retrieval” means

Retrieval is the step where the engine decides whether your pages belong in the context window.

You can be indexed and still not be retrieved.

The retrieval asset portfolio

Asset type	What it does	Example prompts it wins
Definition page	Establishes the canonical explanation	”What is X?” “How does X work?”
Comparison page	Controls the tradeoff story	”X vs Y” “best X for Y”
Criteria / process page	Provides decision rules	”How to choose X” “What should I look for in X?”
Objection / FAQ page	Handles the skepticism	”Is X worth it?” “Does X still work in 2026?”

Internal linking rules (simple)

• Your definition page should link to comparison, criteria, and objection pages.
• Your comparison page should link back to the definition and forward to criteria.
• Your criteria page should link to the pages that prove your criteria.
• Don’t bury these in a footer. Put them in-body where the engine can read the relationship.

The retrieval test

If you want a fast reality check, ask:

• Do we have a page that answers the definition question directly.
• Do we have a page that answers the comparison question directly.
• Do we have a page that gives decision criteria directly.

If the answer is no, you’re asking the engine to assemble your viewpoint from fragments.

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Appendix C: Extractability Spec

If you only take five things from this appendix, take these:

• Start each section with the answer.
• Write paragraphs that make sense when copied alone.
• Use lists and tables to remove ambiguity.
• Put constraints and caveats next to the claim.
• Add at least one quote block per major section.

Expand Appendix C: Extractability Spec

The goal

AI systems don’t “rank” your article. They lift chunks.

Your job is to make the best chunk the easiest chunk to lift.

Definition: Extractability

A passage is extractable when it:

• answers a question without needing surrounding context
• is specific enough to be useful
• includes the constraints needed to avoid misquotation
• can be copied into an answer with minimal rewriting

The 10 Rules

1) Start with the answer. In the first 1-2 sentences under a header, give the direct answer.

Bad: “There are many factors to consider…”

Good: “To optimize for AI search, you need to (1) be accessible to crawlers, (2) be selected as context, and (3) publish passages that are easy to quote accurately.”

2) One idea per paragraph. Paragraphs should be 1-4 sentences. If you’re switching concepts, start a new paragraph.

3) Make sections self-contained. Write as if the model will only read this section. Restate the subject. Avoid “this/that/it” without a noun.

4) Prefer concrete nouns over pronouns.

Bad: “This improves it.”

Good: “This improves AI citation likelihood because the model can lift the paragraph as a standalone source.”

5) Use “constraint-first” language when it matters. If advice only applies in certain cases, name the condition.

Pattern: “If X is true, do Y. If X is false, do Z.”

Example: “If you block AI crawlers in robots.txt, you reduce your chances of being cited. If you want visibility in AI answers, allow access and monitor crawl activity.”

6) Add “caveats that travel.” Caveats must sit next to the claim. Don’t put nuance 800 words later.

Example: “AI Overviews can cite pages outside the top 10, but top-ranking pages still tend to be overrepresented for many queries.”

7) Prefer lists and tables for multi-factor explanations.

Table pattern that gets quoted well:

Surface	What it is	What you optimize for
Google AI Overviews	Summary block in SERP	clear definitions, list-friendly structure, corroborated facts
AI Mode	Conversational Google surface	coverage across sub-questions, self-contained passages, durable citations

8) Name your concepts. If you have a framework, give it a stable name. “ARENA Framework” is better than “these steps.” Then reuse the exact phrase consistently.

9) Put the most quoteable blocks where they’ll be found. Quoteable blocks should live immediately under relevant H2/H3, near the top of the article for the main definition, and in a dedicated “framework” section.

10) Write like you expect to be quoted. Avoid vague superlatives, empty adjectives, claims without mechanism. Prefer mechanisms, definitions, criteria, examples.

Anti-patterns (what to stop doing)

• Throat-clearing intros under every header
• Meandering “it depends” paragraphs that never land a point
• Pronoun soup (“this/that/it”) that breaks when excerpted
• Nuance at the end (caveats separated from claims)

Quick QA checklist (before publish)

For each H2 section:

• The first paragraph can stand alone as an answer.
• If someone copied the first 80-120 words, it would still be true.
• Constraints and caveats are next to the claim.
• A list or table is used where it reduces ambiguity.

Optional: “Quote blocks” (high-leverage pattern)

Once per major section, add a 2-4 sentence block designed to be lifted.

Pattern: Definition + mechanism + constraint

Example: “AI search optimization is the practice of making your content retrievable and quoteable in AI-generated answers. Retrieval gets you into the context window. Extractability gets your passage lifted. Authority keeps you showing up as sources change.”

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Appendix D: Name (Attribution) Engineering

If you only take five things from this appendix, take these:

• Bind your brand to key definitions once.
• Name your frameworks and keep the names stable.
• Put a brand cue next to quote blocks.
• Make authorship real and consistent.
• Turn claims into criteria so they’re easier to cite.

Expand Appendix D: Name (Attribution) Engineering

The problem

A lot of brands show up as “a source.” Very few brands show up as the name attached to the idea.

In AI answers, attribution failure looks like:

• your concept is paraphrased with no brand mention
• your data is used but credited to someone else
• your framework is described generically (“a five-step process”) instead of as your named model

The principle

Name means brand attachment.

Attribution is not a schema problem. Schema helps machines understand your page.

Attribution engineering helps machines (and humans) associate the claim with the entity.

8 practical rules

1) Bind the brand to the definition. When you define something, explicitly include the brand or founder once.

Pattern: “At [Brand], we define X as…” or “In Yoyao’s work, X means…”

Use sparingly. Once per major definition is enough.

2) Name your frameworks and keep names stable. ARENA Framework is a stable handle. Don’t rename it three times in one article.

3) Put the brand cue next to the quote block. If you have a 2-4 sentence quote block designed to be lifted, add a local attribution cue.

Example: “This is the ARENA Framework we use at Floyi to diagnose why brands disappear from AI answers.”

4) Don’t hide authorship. Author page exists. Specific bio (not generic). Experience signals that are verifiable.

5) Use “sameAs” consistency everywhere. Consistency beats cleverness. Same brand name across site, socials, directories. Same founder name across bylines and profiles.

6) Use citations like a professional. If you reference stats, cite sources. Not because “E-E-A-T,” but because it makes your block safer to quote and easier to corroborate.

7) Turn claims into criteria. General advice is hard to credit. Criteria and frameworks get attributed.

Pattern: “If you want X, check these 5 criteria.”

8) Make your “signature” repeatable. Repeat one signature model across multiple posts. The second time a system sees it, it’s no longer a one-off paragraph.

Copy templates (drop-in)

Template A: Definition “AI search optimization is the practice of making your content retrievable and quoteable in AI-generated answers. In Yoyao’s framework, that breaks into five steps: Access, Retrieval, Extractability, Name, and Authority.”

Template B: Diagnostic “If you used to appear in AI Overviews but stopped, it’s rarely random. It’s usually a failure in one ARENA step: you lost access, you stopped getting retrieved, your best passages became less extractable, your brand stopped being attached to the claim, or you lost authority as sources rotated.”

Template C: Executive takeaway “For founders, the goal isn’t ‘rank #1.’ It’s to become the default source the answer engines pull from, even when no click happens.”

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Appendix E: Authority (Reinforcement) Checklist

If you only take five things from this appendix, take these:

• Track the same prompt set weekly.
• Log citation URLs and accuracy, not just mentions.
• Refresh when you drop for two weeks or get misrepresented.
• Protect your best quote blocks and keep definitions stable.
• Build reinforcement off-site so you don’t drift.

Expand Appendix E: Authority (Reinforcement) Checklist

What durability is (and why it’s different)

Authority is what keeps you showing up. Durability is the system that protects that authority over time.

In AI search, you can lose visibility even if your rankings don’t move.

That’s because sources rotate, new content enters the mix, recency thresholds shift, and the model finds a “cleaner chunk” somewhere else.

Durability is your anti-drift system.

A. Drift detection (weekly)

1) Track the same prompt set every week.

• 20-50 prompts, grouped by intent cluster
• Include: definitional prompts, comparison prompts, “best X for Y” prompts, objection prompts

Log per prompt: cited sources, your presence/absence, which URL, snippet accuracy score (0 = wrong, 1 = partially right, 2 = correct).

Owner: SEO/Content Strategy

2) Monitor competitor co-mentions.

• If a competitor is consistently mentioned alongside you, it’s a retrieval adjacency signal.
• If a competitor replaces you, it’s often a chunk-quality or corroboration issue.

Owner: SEO/Content Strategy

B. Refresh triggers (when to update content)

Update when:

• You drop from citations on a high-value prompt cluster for 2+ consecutive checks
• The model cites newer sources for the same question
• Your claim is represented incorrectly (accuracy score 0)
• A new competitor publishes a cleaner “quote block” version of your point

Owner: Editorial + Content Strategy

C. On-site durability moves

3) Protect your best quote blocks.

• Keep definitions stable. Don’t rewrite framework names casually. Add a changelog note if you materially update claims.

Owner: Editorial

4) Reinforce internal linking to your retrieval assets.

• Definitions should point to comparisons, criteria/process pages, and FAQs. This keeps the site’s “topic graph” consistent.

Owner: SEO

D. Off-site reinforcement (monthly)

5) Corroborate your key claims elsewhere.

Aim for 3 tiers:

• Tier 1 (easy): consistent profiles, directories, review sites
• Tier 2 (medium): podcasts, guest posts, conference decks, YouTube interviews
• Tier 3 (hard): research, benchmarks, original datasets

Owner: PR/Founder marketing

6) Win the “list and comparison” layer (selectively).

• You don’t need to chase every list. But you do need to win the lists that define your category.

Owner: Founder/Marketing

E. Representation integrity

7) Fix misrepresentation with better constraints.

When AI gets you wrong, it’s usually because your content allowed an overgeneralization, buried a constraint, or lacked a crisp definition.

Fix: move constraints next to claims, add caveats that travel, add a quote block that is unambiguous.

Owner: Editorial

Durability score (simple rubric)

• 0-1: unstable (frequent drops, misquotes)
• 2: improving (present but inconsistent)
• 3: stable (present across most prompts)
• 4: dominant (first-cited, consistent, accurate)

Use this to decide where to invest reinforcement effort.

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Appendix F: Weekly AI Search Visibility Measurement Template

If you only take five things from this appendix, take these:

This template is for tracking AI search visibility: citations, mentions, accuracy, and competitor co-mentions across Google AI Overviews, AI Mode, and chat engines.

For exec reporting, pair it with Floyi’s authority pillars: Content Authority, Market Authority, and AI Authority (mentions and citations).

• Define a prompt set by intent cluster.
• Track citations and the exact URLs cited.
• Score accuracy from 0 to 2.
• Watch competitor co-mentions to spot replacement.
• Assign owners by ARENA step.

Expand Appendix F: Weekly AI Search Visibility Measurement Template

1) Executive summary (5 lines max)

• Wins this week
• Losses this week
• Biggest drivers (what changed)
• Risks (drift / misrepresentation)
• Next actions (owners + deadlines)

2) Coverage snapshot (by surface)

Surface	Coverage this week	WoW change	Notes
Google AI Overviews
Google AI Mode
ChatGPT
Perplexity
Gemini
Claude
Bing Copilot
Grok

Coverage definition (pick one and stay consistent): % of tracked prompts where you are cited/mentioned, or count of prompts where you are cited/mentioned.

3) Prompt set (by intent cluster)

Intent cluster	Prompt	Surface	Are we cited? (Y/N)	URL cited	Competitors cited	Accuracy score (0-2)	Notes

Accuracy score: 0 = wrong / misleading, 1 = partially right, 2 = correct.

4) Drift log (what changed)

Prompt cluster	What changed	Hypothesis	Fix	Owner

5) Pipeline diagnosis (where the problem lives)

Check the box that best explains this week’s losses:

• Access (Eligibility)
• Retrieval
• Extractability
• Name (Attribution)
• Authority (Reinforcement)

6) Resourcing (owners + backlog)

Workstream	This week’s work	Next week’s work	Owner
Access
Retrieval
Extractability
Name
Authority

7) North star metric (optional)

If you want a single exec metric: AI Mention Share = mentions of us / (mentions of us + mentions of top 3 competitors). Track it weekly per surface.