The 19-point checklist behind every recommendation.
Voxaris applies the same 19-signal Answer Engine Optimization checklist to every audit and retainer. Each signal is grounded in either peer-reviewed GEO research or first-party measurement across the businesses we’ve audited. The list is public so you can verify it, replicate it, or hold us accountable to it.
How the 19 points group
How do the 19 points group into a composite score?
The composite AI Visibility Score is a weighted average across six dimensions. Each dimension corresponds to a distinct citation signal AI engines weight differently — AI Visibility (25%), Brand Authority (20%), Content E-E-A-T (20%), Technical SEO (15%), Schema Markup (10%), Platform Presence (10%).
| Dimension | Weight | What it measures |
|---|---|---|
| AI Visibility | 25% | Live citation share + per-engine extractability + citation velocity |
| Brand Authority | 20% | Third-party entity recognition: Wikidata, Reddit, sameAs, off-site authority anchors |
| Content E-E-A-T | 20% | Question-form headings, 134–167 word answer passages, author bylines, inline citations |
| Technical SEO | 15% | AI crawler allowlist, server-side rendering, IndexNow, freshness signals |
| Schema Markup | 10% | FAQPage + speakable, Organization + LocalBusiness, per-page Service / Article / BreadcrumbList |
| Platform Presence | 10% | llms.txt completeness, Google Business Profile, citation directories |
The full checklist
What are the 19 signals Voxaris checks?
Each point names a signal, explains why AI engines weight it, and describes how Voxaris implements the fix. The order roughly tracks impact-to-effort, not strict priority.
AI Visibility
Citation share across the six major AI engines
Why it matters: The headline metric. Every other point on this list serves this one. Citation share = the percentage of relevant prompts in your category that name your business inside an AI answer.
How we implement: Voxaris runs 36 standardized prompts per week (six per engine across ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews, and Bing Copilot) and tracks citation count, ranked position, and source-overlap with the top three competitors.
AI Visibility
Per-engine extractability
Why it matters: Each AI engine has different content preferences. Bing Copilot weights schema density. Perplexity weights primary sources and Reddit footprint. Gemini weights topical clustering. ChatGPT weights entity recognition.
How we implement: We profile per-engine performance separately and prioritize fixes for the engines where the gap is widest. Not every fix moves every engine.
AI Visibility
Citation velocity
Why it matters: How fast new citations accumulate over time. A site that gains 5 new citations per month is on a different trajectory than one stuck at 0 — even if absolute counts look similar in a single snapshot.
How we implement: Tracked weekly in the Voxaris dashboard. Twelve months of history retained.
Brand Authority
Wikipedia / Wikidata entity
Why it matters: ChatGPT, Perplexity, and Gemini all cross-reference Wikidata for entity confirmation before citing a brand. No Q-item means the brand may not exist to the engine even if the website is fully indexed.
How we implement: We submit a Wikidata Q-item with founded-date, instance-of:business, and sameAs to LinkedIn/X/site. Wikipedia article only when notability threshold can be met (otherwise it's a deletion magnet).
Brand Authority
Reddit / forum footprint
Why it matters: Perplexity weights Reddit citations heavily. Brands that appear in r/SEO, r/smallbusiness, or category-specific subreddits get extracted more often, even on non-Reddit prompts.
How we implement: Authentic discussion seeding only — never astroturf. We help clients identify substantive threads where their experience genuinely contributes.
Brand Authority
sameAs entity graph
Why it matters: Six or more authoritative sameAs links inside the Organization JSON-LD give AI engines a triangulated entity confirmation. Three or fewer is the citability ceiling for most local businesses.
How we implement: We expand sameAs to LinkedIn, X, GitHub, Crunchbase, YouTube, Google Business Profile, Wikidata, and any industry-relevant directory (Clutch, G2, BBB, etc.).
Brand Authority
Off-site authority anchor
Why it matters: One legitimate guest post, podcast appearance, or industry directory listing is worth more to AI authority weighting than ten more on-site landing pages. AI engines weight third-party validation heavily.
How we implement: We pitch one guest post or one podcast appearance per quarter as part of the content tier. The retainer alone does not include this.
Content E-E-A-T
Question-form headings
Why it matters: Headings phrased as questions are 2.1× more citable per the 2024 Georgia Tech GEO research. AI engines parse headings to match user query intent.
How we implement: Every retainer page (FAQ, product, service, blog) gets question-form H2/H3 headings. We rewrite existing headings on the site as part of week-1 fixes.
Content E-E-A-T
134–167 word answer passages
Why it matters: Bortolato's 2025 analysis of Google AI Overview citations found extracted passages cluster sharply at 134–167 words. Shorter passages get truncated; longer ones get sampled, not quoted.
How we implement: Content engine produces 4 question-form FAQ pages per month, each with answer passages calibrated to that length window. Existing pages get retrofitted in week 2.
Content E-E-A-T
Author bylines + Person schema
Why it matters: AI engines weight content with named author bylines and Person schema higher on E-E-A-T. Anonymous content is downweighted, especially in YMYL (your-money-your-life) categories.
How we implement: Every blog post gets a Person-schema author byline linked back to the Organization. Founder bio is published with credentials.
Content E-E-A-T
Inline citation discipline
Why it matters: Content with inline citations to authoritative sources is cited 20–25% more often by Perplexity and ChatGPT (per the IIT Delhi 2024 study). AI engines prefer to cite citations.
How we implement: Every long-form post links to primary sources (research papers, government data, named studies). We avoid "studies show" without naming the study.
Technical SEO
AI crawler allowlist in robots.txt
Why it matters: GPTBot, ClaudeBot, PerplexityBot, OAI-SearchBot, Google-Extended, Applebot-Extended, CCBot, Bytespider, and Meta-ExternalAgent must be explicitly allowed. Default "User-agent: *" is sometimes overridden by hosting platforms blocking AI bots.
How we implement: We deploy an explicit allow-list robots.txt naming all 14 AI crawlers, plus a sitemap declaration. Verified live as each user-agent.
Technical SEO
Server-side rendering / prerendered HTML
Why it matters: Most AI crawlers do not execute JavaScript. SPA shells return empty <body> to ClaudeBot or GPTBot. Prerendered HTML is the only reliable way to ensure crawlers see real content.
How we implement: For Vite/SPA sites we add a puppeteer prerender step. For Next.js or Nuxt, SSR is configured. Verified by curl against each AI user-agent.
Technical SEO
IndexNow + freshness signals
Why it matters: Bing Copilot (and downstream Yandex/Naver) re-fetches pages within minutes of an IndexNow ping. Lastmod timestamps on sitemap entries reinforce freshness.
How we implement: We wire IndexNow into the deploy pipeline. Every push automatically pings Bing/Yandex/Naver. Sitemap lastmod is auto-updated.
Schema Markup
FAQPage + speakable on every Q&A page
Why it matters: FAQPage schema is the single most-extracted schema type by AI Overviews and Bing Copilot. Adding speakable selectors lets voice-AI surfaces extract specifically marked passages.
How we implement: Every Voxaris page with FAQ content gets FAQPage JSON-LD with speakable cssSelector pointing at the answer DOM. Validated against schema.org spec.
Schema Markup
Organization + LocalBusiness multi-typed
Why it matters: Local service businesses need both Organization (entity) and LocalBusiness (geo) signals. AI engines reconcile these against Google Business Profile and Wikidata before citing.
How we implement: Multi-typed Organization+LocalBusiness+ProfessionalService block emitted on every page with consistent @id, founder Person linked, areaServed listed, GeoCoordinates included.
Schema Markup
Service / Article / BreadcrumbList per page
Why it matters: Page-specific schema (Service on product pages, Article on blog posts, per-page BreadcrumbList reflecting actual path) gives AI engines a clean signal of what each URL is for. Site-wide schema alone is insufficient.
How we implement: Every product page emits a page-scoped Service block. Every blog post emits Article + Person author + BreadcrumbList. No reusing the site nav as a BreadcrumbList.
Platform Presence
llms.txt with full structure
Why it matters: llms.txt is the emerging spec for telling AI models how a site is organized. Done well, it serves as a direct prompt-engineering surface for AI engines that respect it (currently: ChatGPT, Perplexity, partial Claude).
How we implement: We publish llms.txt and llms-full.txt with company description, founder, key terms with definitions, service tiers with prices, verified results, FAQ, and resources. Updated on every deploy.
Platform Presence
Google Business Profile completeness
Why it matters: Google AI Overviews and Gemini both pull heavily from GBP for local intent queries. Complete GBP with category, hours, services, photos, and review responses is the floor for local AEO.
How we implement: Every retainer includes GBP audit and completeness fixes — service area, categories, hours, products, services, photo cadence, and review-response template. Verified in dashboard week 1.
The three-pool ladder
Not all citations are equal. We score three pools so the climb is visible.
AI engines aren't ranking — they're retrieving and summarizing. Different query densities surface differently: specialty queries reward content depth on the procedure, niche queries reward identity signals (language, schedule, demographic), broad queries get compressed to the few entities the model has the strongest priors on. Most clients earn niche citations within 90 days, climb specialty over 6 months, and reach broad only with sustained authority work. Three pools, three different moats, three different fixes.
Niche
Typical mention ratePractice-side identity — language, sub-geo, schedule, demographic, payment model.
e.g. "Portuguese-speaking dentist metro-west", "after-hours emergency dentist Orlando"
Where clients win: Most clients win here first — the smallest competitor set, the most defensible moat.
~60% typical for established clients
Specialty
Typical mention rateService-side narrowing — the query names a procedure, certification, or product line.
e.g. "where to get Invisalign Orlando", "dental implants Winter Park"
Where clients win: Citation tier focus — every practice that offers the service is competing, so content depth on the procedure is the differentiator.
~45% typical for established clients
Broad
Typical mention rateGeneric category, no geography or qualifier — the hardest pool to crack.
e.g. "best dentist in Orlando", "best HVAC company"
Where clients win: Authority tier territory only — sustained Wikipedia + research work.
~15% typical for established clients
What each tier reports
Every retainer tier scores all three pools — what changes by tier is the depth of analysis and the work actively driving each pool. Each tier ships a single tier-named report each month: Visibility Report, Citation Report, or Authority Report.
| Tier | What the monthly report covers |
|---|---|
| Visibility | Score trend across all three pools, competitor delta, hallucination scan, AI crawler summary, content decay flags, drop alerts. |
| Citation | Everything in the Visibility Report, plus the active citation work driving each pool — directories, reviews, Reddit, content production, outreach, Perplexity Pages, Wikidata maintenance, sole-source mining. |
| Authority | Everything in the Citation Report, plus per-engine breakdown across all 6 engines, prompt experiments, competitive intelligence overlay, quarterly Geographic Ladder + Share-of-Model executive view. |
Research basis
Where does the Voxaris methodology come from?
The 19 signals are grounded in three sources: peer-reviewed GEO research from Princeton, Georgia Tech, and IIT Delhi (2024); third-party AI Overview citation-length analysis (Bortolato 2025); and Voxaris's own first-party measurement across audited businesses.
Aggarwal et al. (2024). Generative Engine Optimization: A Framework for Optimizing Content for AI Engines.
Princeton University, IIT Delhi, Georgia Tech
GEO-optimized content achieves 30–115% higher visibility in AI-generated responses. Definition patterns increase citation rate by 2.1×. Statistical density increases citation by ~40%.
Bortolato (2025). Google AI Overview Passage-Length Analysis.
Independent
AI Overview citation passages cluster sharply at 134–167 words. Shorter passages truncate; longer ones get sampled, not quoted verbatim.
Voxaris first-party measurement (2026).
Voxaris, LLC
Average client AI Visibility Score lift of 15–25 points within first 30 days of retainer. Bing Copilot citations surface fastest (avg day 14); ChatGPT citations slowest (avg day 60).
See your business scored against the 19-point checklist.
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