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AI Visibility Playground

See how ChatGPT, Claude, Perplexity, and Gemini decide which brands to mention, cite, and recommend.

Most founders think AI visibility is about ranking higher. It is not. AI visibility is about becoming the clearest, most trusted, and easiest-to-recommend answer for the right prompt. This page is the source of truth for how each answer engine works, what the evidence actually says, and what to fix first.

Run Free AI Visibility Audit →View Sample Audit ↘

By Matthew Lin, Founder, AudFlo · Updated 13 Jun 2026

Learn how AI engines recommend companies

See what influences recommendations across each engine.

The core framework

Recommendation Confidence = Accessibility × Classification × Evidence × Entity Confidence × Contextual Fit

These five factors multiply. If any one is near zero, the whole score collapses. A brand can have great proof and still be invisible if crawlers cannot reach the page.

Accessibility

Can AI crawlers reach and read your site? If your content is blocked, slow, or JavaScript-only, you are invisible before anything else is judged.

Write an llms.txt file →

Classification

Can the AI tell what category your product is in? A clear category in your headline beats a clever slogan every time.

Why startups are invisible to ChatGPT →

Evidence

Can your claims be checked? Named testimonials, case studies, reviews, and original data turn a claim into a fact an AI can cite.

FAQ schema for AI answers →

Entity Confidence

Does the AI recognize your brand as a real, distinct thing, separate from similarly named companies?

Why comparison pages win →

Contextual Fit

Are you the best match for the exact prompt? Broad claims lose. Specific positioning for a specific buyer wins.

How to get recommended by ChatGPT →

This is the model. AudFlo measures it as your AI Visibility Score across four weighted pillars. See exactly how these five factors map to the four pillars, and how the Evidence Ladder is scored, in the methodology.

How sure are we? We label every claim

ConfirmedDocumented publicly by the platform itself.
Strong evidenceBacked by published docs or repeated independent testing.
Observed patternSeen consistently in third-party analysis, not officially confirmed.
HypothesisA single test or one vendor. Treat as a lead to verify.
UnknownNot knowable from outside the company. We do not guess.
ChatGPTStrong evidence

ChatGPT: Recommendation Confidence

ChatGPT does not simply rank websites. It selects answers. To recommend a brand, ChatGPT needs enough confidence that the brand is accessible, understandable, verifiable, and relevant to the prompt.

OpenAI publishes how its search crawler works, so the access rules here are well documented. How it picks one brand over another is less public, so we mark selection mechanics as observed.

How ChatGPT decides

Think of it as five gates. ChatGPT has to clear all five before your name is safe to put in front of a user. SEO helped websites get found. This is about getting chosen.

  1. 1. Access

    Can ChatGPT reach you? Your site must allow OpenAI’s search crawler, especially OAI-SearchBot. Block it and you lose summaries, snippets, citations, and links.

  2. 2. Understand

    Can it tell what you are? Category, audience, use case, and outcome have to be in plain words on the homepage. "AI visibility audit platform for founders" beats "Grow smarter with AI."

  3. 3. Verify

    Can it trust you? It looks for testimonials, case studies, pricing, a founder or about page, comparisons, documentation, reviews, and a changelog.

  4. 4. Fit

    Are you the best match for the exact prompt? "Best SEO tool" is broad. "Best AI visibility tool for indie hackers under $30 a month" is where specific positioning wins.

  5. 5. Cite

    Can it summarize or quote you? ChatGPT search returns answers with source links and citations, so answer-first, structured, factual pages get pulled in.

What it rewards

  • A clear product category
  • Crawlable public content (OAI-SearchBot allowed)
  • Specific audience positioning
  • Strong evidence: case studies, named testimonials, original research
  • Comparison pages
  • Consistent entity signals

What hurts visibility

  • Vague positioning and generic homepage copy
  • No proof or founder credibility
  • Weak category language
  • Key content hidden behind a login
  • No external validation
Signals and data, with sources+
  • Answers carry source links and citationsConfirmed

    ChatGPT search returns answers with links to the sources it used, so your URL, not just your brand, can be surfaced and clicked.

    Source: OpenAI, ChatGPT search documentation

  • Allow OAI-SearchBot to appearConfirmed

    OpenAI states public sites can appear in ChatGPT search and should not block OAI-SearchBot if they want summaries, snippets, citations, and links.

    Source: OpenAI, crawler documentation

  • Selection favors brands it can classify and verifyObserved pattern

    Across testing, ChatGPT skips products it cannot place in a category or back with proof, and names the option that is easiest to describe with confidence.

    Source: Industry AEO analysis, 2025–2026

  • The exact recommendation functionUnknown

    How ChatGPT weighs one verified, well-classified brand against another is not published. We optimize the inputs we can see, not a secret formula.

    Source: Not knowable from outside OpenAI

What AudFlo checks+
  • Is the product category clear in the H1, title, metadata, and homepage copy?
  • Does the site explain who the product is for?
  • Does the site include evidence, testimonials, or case studies?
  • Does the site have a methodology, FAQ, pricing, or comparison page?
  • Can AI crawlers access the content?

Recommended fixes

  • Rewrite the hero to include category, audience, and outcome
  • Add named testimonials
  • Add a sample audit or case study
  • Publish comparison pages
  • Publish original data or benchmarks
  • Add FAQ and methodology content
ClaudeObserved pattern

Claude: Entity Recognition + Third-Party Validation

Claude visibility has two channels: what Claude knows from training, and what Claude retrieves through live web search. They run on completely separate rails, and only the live channel is something you can move this quarter.

Confirmed: Claude web search runs on Brave. Observed: it searches selectively and leans on third-party sources. The exact selection function is Unknown and we do not pretend otherwise.

Entity Confidence

Who are you?
What category are you in?
Are mentions of you connected to the same entity?
Can an AI tell you apart from similar names?
Can an AI recommend you confidently?

How Claude decides: two different Claudes

When someone asks "how do I get recommended by Claude," they are really asking about two systems that work nothing alike. Conflating them is the most common mistake in the field.

  1. Channel one: training

    With no web access, a recommendation is a side effect of what is densely and credibly represented in training data. There is no index to submit to, and a knowledge cutoff sits months behind today. A product launched this quarter is effectively absent here.

  2. Channel two: live web search

    When a question benefits from current information, Claude runs a live search, reads what comes back, and builds the answer from the sources it judges most credible. This channel updates in near real time, so your work can show up in days.

  3. The live channel runs on Brave

    This is the one load-bearing fact. Anthropic added Brave Search to its subprocessor list in March 2025, and independent testing matched Claude’s citations to Brave results. Your Brave rank becomes a usable proxy for Claude visibility.

  4. It searches less, and cites carefully

    Claude invokes search on far fewer prompts than ChatGPT, tends not to re-rank Brave’s results, and is skeptical of content that reads like marketing or an engineered "best tools" listicle.

  5. Entity recognition is the real bottleneck

    Claude recommends a name it can connect to a category through independent corroboration. A new brand, a name collision, or self-only claims break that connection, so it hedges or leaves the name out.

What it rewards

  • A clear entity identity
  • Strong brand-to-category association
  • Third-party mentions and independent validation
  • Brave-visible pages
  • Verifiable, specific claims
  • Non-marketing language with honest tradeoffs

What hurts visibility

  • A new brand with no external footprint
  • Name confusion with similar brands
  • Only self-published claims
  • Over-optimized "best tool" listicles
  • Vague superlatives
  • Weak category association
Signals and data, with sources+
  • Web search is powered by Brave SearchConfirmed

    Anchor your whole live-channel strategy on your Brave rank, not your Google rank.

    Source: Anthropic subprocessor list, Mar 2025; verified by Simon Willison

  • Searches on roughly a third of promptsObserved pattern

    Versus the large majority for ChatGPT. Fewer searches means fewer, higher-stakes citations.

    Source: Jonathan Clark / Profound, via Search Engine Land, 2026

  • High-80s% overlap with Brave’s top organic resultsObserved pattern

    Because Claude tends not to re-rank, your Brave position closely predicts whether you get cited.

    Source: Profound and related analyses, 2025–2026

  • About two-thirds of AI citations are third-partyObserved pattern

    Earned media beats owned media. Self-description rarely earns a citation when corroboration is missing.

    Source: Erlin dataset, 2026

  • 8+ verifiable attributes correlate with far more citationsHypothesis

    Direction looks right: structured, checkable facts help. Treat the exact multiple as one vendor’s number.

    Source: Erlin data, 2026 (single vendor)

Every claim, sorted by how sure we are+

Sort any claim about Claude into one of these tiers before it goes into a strategy. A guide that admits the boundary of what is knowable outlasts one that claims a secret formula.

Claude web search is powered by Brave Search

Confirmed

State without hedging. Anchor your live-channel strategy on Brave, not Google.

Anthropic subprocessor list, Mar 2025; verified by Simon Willison

Every web-search answer carries inline citations

Confirmed

Your URL, not just your brand, can be surfaced and clicked.

Claude Help Center, web search docs

Claude decides automatically when to search

Confirmed

You cannot force a search from the outside. Earn the credibility instead.

Anthropic product launch coverage

Searches on ~1/3 of prompts vs the majority for ChatGPT

Observed pattern

Expect fewer, higher-stakes citations.

Jonathan Clark / Profound, via Search Engine Land, 2026

Claude tends not to re-rank Brave’s results

Observed pattern

This is what makes Brave rank a usable proxy.

Same Profound analysis

High-80s% overlap between Claude citations and Brave top results

Observed pattern

Justifies tracking Brave rank as a leading indicator.

Profound and related analyses, 2025–2026

~2/3 of AI citations come from third-party sources

Observed pattern

Argue for earned media over owned media.

Erlin dataset, 2026

8+ structured attributes correlate with several times more citations

Single-vendor test

Use the direction, not the exact multiple.

Erlin data, 2026

Most cited URLs sit under a blog path; many use listicle patterns

Single-vendor test

A directional hypothesis to test, never a confirmed preference.

Oltre dataset, 2026

The exact source-selection function and weighting

Unknown

Say you do not know. It makes the rest of your guide more trustworthy.

Not knowable from outside Anthropic

What AudFlo checks+
  • Does the brand name clearly connect to the category?
  • Are there comparison pages and third-party mentions?
  • Is the brand distinguishable from similarly named entities?
  • Are claims specific and verifiable?
  • Is the content written like useful guidance rather than marketing fluff?

Recommended fixes

  • Strengthen the brand-to-category line everywhere it appears
  • Add Organization and SoftwareApplication schema
  • Build third-party mentions on credible sites
  • Create founder-led original research
  • Track visibility for a fixed set of Claude prompts
  • Check your Brave rank for target queries
PerplexityObserved pattern

Perplexity: Extractable, Fresh, Citable Sources

Perplexity is a retrieval-augmented engine. It pulls live sources, synthesizes one answer from five to ten of them, and cites passages. Your content has to survive being broken into a single quotable passage, not read as a whole page.

Perplexity behaves like a retrieval and citation engine. The pipeline, gates, and signals below come from published guidance and repeated third-party testing, not an official ranking spec.

How Perplexity decides

Every answer runs through the same six-stage pipeline. You are cited as a passage, not as a page, so your content must survive being fragmented and recombined.

  1. 1. Decompose

    Your question is split into several search-optimized sub-queries.

  2. 2. Search

    Parallel live web searches run across those sub-queries.

  3. 3. Retrieve

    The top results per sub-query are retrieved and indexed.

  4. 4. Read

    Retrieved content is passed to the language model as context.

  5. 5. Synthesize

    One answer is built that combines five to ten sources and resolves conflicts between them.

  6. 6. Cite

    Inline citations are attached to the specific passages used.

The three gates to citation

Fail any one gate and you are out, no matter how well you do on the others.

  1. Gate 1: Discoverability

    PerplexityBot must be able to crawl you. Server-rendered HTML is required, pages should load in under three to four seconds, and blocking the crawler removes you entirely.

  2. Gate 2: Extractability

    Your content must contain discrete, quotable passages. Dense narrative fails. Every paragraph should make sense when quoted on its own.

  3. Gate 3: Trustworthiness

    Enough external validation that Perplexity is confident attributing the claim to you: author identity, corroboration, and no contradictions with high-authority peers.

What it rewards

  • Fresh content
  • Short, answer-first sections
  • Strong schema (FAQPage, Article, Person)
  • Clear citations and original data
  • Tables and structured comparisons
  • Third-party sources like Reddit, LinkedIn, GitHub, docs, and reviews

What hurts visibility

  • Long narrative essays
  • Stale content for a fast-moving topic
  • No structured data
  • Anonymous authors
  • Unsupported claims that contradict high-authority peers
  • Slow pages or JavaScript-only content
Signals and data, with sources+
  • Article + FAQPage + Person schema: ~89% higher citation probabilityObserved pattern

    Schema is a comprehension signal. It tells Perplexity what entity it is reading and whether it can trust the attribution.

    Source: Pepper Content, Feb 2026

  • FAQPage schema alone: large citation-rate liftObserved pattern

    One study moved citation rate from a ~15% baseline toward ~41% with FAQPage schema in place.

    Source: KOIRA citation-rate study, 2026

  • Reddit is ~46.7% of top-10 citations; brand-owned content is lastObserved pattern

    A user asking for the best tool is more likely to see you in a Reddit thread or expert LinkedIn post than on your own site.

    Source: Pepper Content top-10 citation analysis, 2026

  • Original first-party data is cited ~3.2x more oftenObserved pattern

    Perplexity cannot synthesize a number that only exists on your page from anywhere else, so it must cite you to use it.

    Source: WP SEO AI analysis, 2026

  • Freshness ~40% of the signal on competitive queriesHypothesis

    Content not updated in six months reportedly loses around 3x citation probability; for AI and finance topics the window is days.

    Source: WebCoreLab deep dive, 2026 (vendor-reported)

  • The exact ranking weightsUnknown

    The hybrid neural-symbolic stack and its weights are proprietary. We optimize the visible inputs.

    Source: Not knowable from outside Perplexity

What AudFlo checks+
  • Is the content answer-first?
  • Can paragraphs be extracted as standalone passages?
  • Does the page include schema?
  • Does the page include author and entity signals?
  • Are there clear tables, FAQs, or comparison blocks?
  • Is the content fresh enough for the query type?

Recommended fixes

  • Add direct answers in the first 100 to 200 words
  • Use question-based headings
  • Add FAQPage, Article, and Person schema
  • Publish original data
  • Add comparison tables
  • Refresh AI-related content regularly
GeminiObserved pattern

Gemini: Passage Extraction + Information Gain

Gemini indexes passages, entities, and data structures, not just URLs. It favors content it can extract at the passage level, map to an entity, and back with useful evidence. Generic summaries are filtered out.

Gemini and Google AI Overviews favor passage-level content mapped to entities. The structural rules and evidence tiers below are observed across testing, not an official ranking spec.

How Gemini decides

Traditional search indexed URLs. Gemini indexes passages, entities, and data structures, then filters them for information gain before anything reaches an answer.

  1. Index passages, not pages

    Each section is judged on its own. A page can rank in organic search and still be invisible in AI Overviews.

  2. Filter by evidence tier

    Content runs through a five-tier evidence gate, from proprietary data at the top to commodity summaries that get filtered out.

  3. Demand clean extraction

    A passage that relies on "as mentioned above" or lacks its entity inside the block fails the extraction gate.

  4. Map to entities

    Vague pronouns lose. The passage has to restate the entity so it stays a valid match when pulled out alone.

  5. Reward information gain

    Original data, expert commentary, and first-person experience beat a restated summary of the consensus.

Gemini’s five-tier evidence gate

Where your content sits on this scale drives its share of AI Overview citations. Aim high; avoid Tier 5.

  1. Tier 1: Proprietary empirical

    Primary-source data: internal tests, benchmarks, raw datasets and methods. The strongest signal.

  2. Tier 2: Credentialed expertise

    Commentary tied to a named expert with verifiable credentials, bound to Person schema.

  3. Tier 3: Consensus aggregation

    Heavily cited syntheses that map cleanly to known facts, using comparison tables and clear definitions.

  4. Tier 4: First-person experience

    Genuine "I tested this" accounts with specifics, timelines, and unedited detail. Google’s "hidden gems."

  5. Tier 5: Commodity aggregation

    Generic summaries with zero original data. Actively filtered out by information-gain thresholds. Avoid.

What it rewards

  • Passage-level clarity
  • Entity-specific headings
  • Direct answer blocks
  • Structured tables and original data
  • Expert and first-person experience signals
  • Clear definitions, strong internal linking, and schema

What hurts visibility

  • Generic AI-written summaries
  • Vague headers
  • Pronoun-heavy paragraphs that depend on earlier sections
  • No unique information
  • No expert or experience signals
  • Weak entity anchors
Signals and data, with sources+
  • AI Overviews favor passage-level content mapped to entitiesObserved pattern

    Optimization shifts from ranking a URL to making each passage extractable and entity-anchored.

    Source: Google AI Overviews guidance + industry testing, 2026

  • Commodity summaries are filtered by information-gain thresholdsObserved pattern

    Pages that only restate existing content with no original insight are bypassed to avoid circular training.

    Source: Industry AIO analysis, 2026

  • Context-dependent passages fail extractionObserved pattern

    A block that needs earlier sections to make sense carries zero value when decoupled from the page.

    Source: Industry AIO analysis, 2026

  • Worked-example metrics (14.2% lift, 42% share of voice)Hypothesis

    Numbers like these appear in case-study write-ups as illustrations. Treat them as directional, not benchmarks you should expect.

    Source: Vendor case study, illustrative

  • The exact Gemini ranking specUnknown

    Google does not publish the weighting. We optimize structure, entities, and evidence, which are the visible inputs.

    Source: Not knowable from outside Google

What AudFlo checks+
  • Does each important section have a clear question or entity-based heading?
  • Does the first paragraph answer the heading directly?
  • Can the passage stand alone if extracted?
  • Are entities repeated clearly instead of pronouns?
  • Does the content add information gain beyond a generic summary?
  • Are tables and definitions used properly?

Recommended fixes

  • Rewrite headings as specific questions
  • Put direct answers immediately below each heading
  • Replace vague pronouns with the entity name
  • Add original examples, tests, or benchmarks
  • Use tables for comparisons
  • Add expert or founder commentary

The Evidence Ladder

AI tools do not trust every signal equally. A vague claim is weak. A named customer story is stronger. Original data is strongest.

Stronger, harder to fake

Tier 1Original Research
Tier 2Third-Party Validation
Tier 3Case Studies
Tier 4Testimonials
Tier 5Marketing Claims

Weaker, easy to fake

Tier 1Original research, benchmarks, and proprietary data

The hardest evidence to copy and the easiest to cite. A figure only you have is one an AI must attribute to you.

An original benchmark or dataset built from your own first-party data.

Check: Original data or research present?

Tier 2Independent mentions, reviews, and coverage you do not own

Independent sources carry more weight than anything you say about yourself, because an AI can corroborate them.

A review on a reputable site, a mention in an industry newsletter, or an unlinked brand mention in someone else’s article.

Check: Independent third-party mentions present?

Tier 3Named customer stories with a measurable outcome

Proof from a real, named customer, with a result an AI can repeat back to a user.

"Acme fixed a missing category line and moved up one rank in three weeks."

Check: Named case studies present?

Tier 4Named quotes that vouch for you

A named person vouching for you helps verify trust, but it is still something you publish about yourself.

A quote from a named founder with a link to their company.

Check: Named testimonials present?

Tier 5Unverified claims you make about yourself

The weakest signal, because nothing here can be checked against an independent source.

"The best AI visibility tool for growth."

Check: Unsupported claims detected?

Citation-Ready Blueprint

Perplexity and Gemini both pull passages, not pages. A section wins when it can be lifted out, attributed, and trusted on its own. These five rules turn any page into one an answer engine can quote.

1The proximity rule

Put the question or entity in a heading (H2 or H3). Put the direct answer in the first 35 words of the very next paragraph, before anything else.

2The entity-anchor rule

No ambiguous pronouns. Restate the entity name inside the answer block so it still makes sense when quoted as an isolated string. "It" and "this tool" lose the citation.

3Evidence inside the passage

Put a checkable claim with a number and a source inside the block. "Captured 14% more events (internal audit, 2025)" beats "improves results."

4Structure that extracts

Use tables for comparisons, FAQ blocks for questions, and a short bolded summary at the end of each section. Extractive systems scan these first.

5Schema that confirms identity

Add Article + FAQPage + Person in JSON-LD, with the author Person linked by sameAs to LinkedIn, X, or Crunchbase. Schema tells the engine which entity it is reading.

Copyable: an answer-first, citation-ready section

Drop this pattern into any page. The heading is a question, the first sentence answers it with the entity named, a checkable stat sits in a quote block, and a table gives a clean structure to extract.

<section itemscope itemtype="https://schema.org/TechArticle" id="ai-visibility-audit">
  <h2 itemprop="headline">What does an AI visibility audit check?</h2>

  <p itemprop="abstract">
    <strong>An AudFlo AI visibility audit checks whether ChatGPT, Claude,
    Perplexity, and Gemini can access, classify, verify, and recommend your
    site.</strong> It scores five factors: accessibility, classification,
    evidence, entity confidence, and contextual fit.
  </p>

  <blockquote>
    <p>[Insert your verified benchmark finding and source here.]</p>
  </blockquote>

  <table>
    <thead>
      <tr><th>Factor</th><th>What the audit looks for</th></tr>
    </thead>
    <tbody>
      <tr><td>Accessibility</td><td>Crawlers allowed, server-rendered HTML, fast load</td></tr>
      <tr><td>Classification</td><td>Category and audience in the H1 and metadata</td></tr>
      <tr><td>Evidence</td><td>Case studies, named testimonials, original data</td></tr>
    </tbody>
  </table>
</section>

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "What does an AI visibility audit check?",
  "author": {
    "@type": "Person",
    "name": "Matthew Lin",
    "sameAs": ["https://your-public-profile-url"]
  }
}
</script>

Worked Example: How AudFlo Becomes Easier To Recommend

The same product, described two ways. One is a hook. The other is something an AI tool can classify, verify, and recommend.

Before

“Get Recommended By ChatGPT”

Strong hook, but incomplete classification.

Why AI may struggle

  • It knows the outcome
  • But it may not know the category
  • It may confuse the product with generic SEO, AEO, or content tools
  • It needs more evidence before recommending it
After

“AI Visibility Audit Platform For Founders”

Find out why ChatGPT recommends competitors instead of you, and get the exact fixes to improve recommendation visibility.

Why this is stronger

  • Category is clear
  • Audience is clear
  • Problem is clear
  • Outcome is clear
  • Product is easier to classify and recommend

The cold-start entity problem

A new brand that shares a name with an established one fails the recommendation test for three compounding reasons. The order you fix them in matters.

Problem 1

Absent from the training channel

As a recent launch, the brand is not yet a formed entity in any trained model. There is no fast fix. It resolves only as independent mentions accumulate for a future training run.

Problem 2

Near-zero third-party co-occurrence

The brand name and its category do not yet appear together on credible, Brave-visible sources. Self-assertion is the exact signal models discount. This is the fixable one.

Problem 3

Name-space collision corrupts disambiguation

When the name sits next to a better-established lookalike, ambiguity enters before any positive association can form, so the model hedges or conflates.

The recommendation pathway, in order

  1. 1Resolve the entity first: one name, one category line, Organization and SoftwareApplication schema, and language that addresses the name collision head-on.
  2. 2Fix the foundation it sits on: no contradictory pricing, broken routes, placeholder text, or dead sitemap URLs that make the entity look unreliable.
  3. 3Build third-party co-occurrence on Brave-visible sources, pairing the brand name with its category on pages other people publish.
  4. 4Track Brave rank for target queries as the earliest measurable sign that visibility is moving.
  5. 5Measure inside the conversation: run a fixed set of category prompts on a regular cadence and record whether the brand appears.

Add an evidence layer

Sample auditMethodology pageFounder pageNamed testimonialComparison pagesFAQPricingOriginal benchmark report

Add an external layer

Founder interviewsLinkedIn postsReddit discussionsStartup directoriesTool reviewsIndependent comparisons

The goal is not to trick AI tools. The goal is to make AudFlo easier to access, understand, verify, and recommend.

What To Fix First

Start at the top. The early items are high impact and easy, so they move your visibility fastest.

  1. 1
    Make the category obvious
    Impact: HighDifficulty: EasyTime: ImmediateCheck: Category clarity
    Why your startup is invisible to ChatGPT →
  2. 2
    Make the audience obvious
    Impact: HighDifficulty: EasyTime: ImmediateCheck: Audience clarity
    How to get recommended by ChatGPT →
  3. 3
    Add proof
    Impact: HighDifficulty: MediumTime: 1 to 2 weeksCheck: Evidence and testimonials
    See a sample audit →
  4. 4
    Add FAQ and methodology pages
    Impact: HighDifficulty: MediumTime: 1 to 2 weeksCheck: Support pages
    How FAQ schema gets you into AI answers →
  5. 5
    Add comparison pages
    Impact: MediumDifficulty: MediumTime: 2 to 3 weeksCheck: Comparison coverage
    Why comparison pages win →
  6. 6
    Add schema
    Impact: MediumDifficulty: MediumTime: 1 weekCheck: Structured data
    How FAQ schema gets you into AI answers →
  7. 7
    Publish original research
    Impact: HighDifficulty: HardTime: OngoingCheck: Original data
    The AI Visibility Score, explained →
  8. 8
    Build third-party mentions
    Impact: HighDifficulty: HardTime: 60 to 90 daysCheck: External footprint
    Why comparison pages win →
  9. 9
    Track AI prompts over time
    Impact: MediumDifficulty: EasyTime: OngoingCheck: Prompt monitoring
    The AudFlo methodology →