Contents
What's in this report
Executive summary03
AI visibility scorecard — by platform04
Query-by-query analysis05
Competitor citation gap06
Your 8-lever diagnostic07
Brand entity health08
90-day implementation roadmap09
Next steps10
How this audit works
Built on the same framework as the playbook
This report applies the 8-lever AEO/GEO framework documented in the Citebound 2026 AI Search Whitepaper. Every finding maps to a specific lever — so you know exactly which tactic closes each gap. The levers run in sequence: Foundation first (Levers 1–3), then Content Engine (Levers 4–6), then Advanced (Levers 7–8).
We ran 50 high-intent buyer queries across all five major AI platforms. For each query we logged whether you appeared, where competitors ranked, what sources were cited, and which levers explain the gap. Data was gathered between [start date] and [end date].
Foundation · Levers 1–3
Days 1–30
1. Schema & structured data
2. Brand entity consistency
3. AI crawler accessibility
Content Engine · Levers 4–6
Days 31–60
4. Answer-first content structure
5. Citation density & evidentiary depth
6. Third-party authority building
Advanced · Levers 7–8
Day 90 onward
7. Continuous freshness maintenance
8. Cross-platform monitoring
Executive summary
Your AI visibility is leaking demand.
Out of 50 high-intent buyer queries across ChatGPT, Perplexity, Google AI Overviews, Claude, and Grok, your business was cited in only [X] of them. Your direct competitors are appearing in [Y] queries where you're invisible.
Visibility score
[XX]/100
Industry median: 47
Citation rate
[XX]%
[X] of 50 queries
Competitor gap
[XX]
queries where rivals win
The bottom line
Your business has [X] high-intent buyer queries where you should appear but don't. At an AI search conversion rate of 14.2% (vs 2.8% for traditional organic) and your average customer LTV of $[Z], the visibility gap represents roughly $[###] in lost annual revenue. Most gaps close in 60–90 days. The root causes map cleanly to Levers 1, 2, and 4 of the 8-lever framework.
Top 5 findings
L4
You appear in 0 of 12 "best of" queries — the highest-converting queries in AI search. No answer-first content blocks exist on your service pages. Pages with structured headings are 2.8× more likely to be cited.
Critical
L6
[Competitor] is cited 4× more often across overlapping queries. They have 12 high-authority third-party citations. You have 2. Brands in the top 25% for web mentions get 10× more AI visibility.
Critical
L2
Brand entity inconsistent across 7 sources. LLMs aggregate signals across the open web to build a knowledge graph of your business. Inconsistency makes them cite you less confidently.
Warning
L1
FAQPage and HowTo schema missing on key service pages. FAQPage schema maps directly to question-answer queries — the dominant AI search format across all 5 platforms.
Warning
L5
You appear in [X] comparison queries with positive sentiment. Expert quotes (+41% citation lift) and inline statistics (+30%) can convert this credibility into citation authority quickly.
Strength
Visibility scorecard
Where you stand on each AI platform
Each platform cites differently. ChatGPT weights brand mentions; Perplexity rewards recency; Google AI Overviews correlates with traditional rankings; Claude favors expert-authored depth; Grok pulls from real-time social data. A business cited heavily in ChatGPT may be invisible in Perplexity, and vice versa — which is why multi-platform tracking matters.
Query analysis
All 50 queries. Where you appear and where you don't.
Grouped by intent type. "Best of" and comparison queries are the highest-converting in AI search — and typically where your gap is largest. We'll walk through the most important findings on your strategy call.
High intent: "Best of" queries
"Best [your category] in [city] 2026"
"Top [your service] companies for [use case]"
"Most reputable [your service] providers"
"Recommended [your category] for small business"
Comparison queries
"[Your name] vs [competitor]"
"Alternative to [competitor]"
Note: The full 50-query analysis — including educational, problem, location, and use-case queries — is available as an interactive dashboard linked at the end of this report.
Competitor citation gap
Who AI is recommending instead of you
Citation share is zero-sum within a category. Every point a competitor holds is a point you don't. The top 50 brands in any category capture 28.9% of all AI Overview citations — which means early movers compound their advantage.
[X]
[Client name] (you)
[XX]%
Why competitors are winning — mapped to levers
L6
[Competitor 1] has 12 high-authority third-party citations on publications LLMs trust. You have 2. This is the heaviest lift — and where the largest moats get built.
Gap
L4
[Competitor 2] uses answer-first content blocks. Direct 40–60 word answers at the top of every service page. 55% of AI citations come from the first 30% of page content.
Gap
L2
[Competitor 1] has a Wikidata entry with consistent entity data. LLMs use Wikidata as an authoritative anchor point. You don't yet — buildable in 60 days.
Gap
L5
You beat [Competitor 3] on review sentiment. Expert quotes (+41% lift) and inline statistics (+30%) can turn your existing credibility into citation authority quickly.
Strength
8-lever diagnostic
Your score across every lever that drives AI citations
The Princeton GEO research identified the specific content and authority signals that increase citation probability. The 8-lever framework translates that research into executable tactics. Here's where you stand on each one — and what fixing it is worth in estimated citation lift.
Weak
L1
Schema & structured data — FAQPage, HowTo, and Organization schema missing on key pages. 88% of websites still don't implement schema. Fast implementation, significant citation lift.
+8–14 pts
Weak
L2
Brand entity consistency — Business name and facts vary across 7 sources. LLMs build a knowledge graph of your business by aggregating these signals. Inconsistency = lower citation confidence.
+6–12 pts
Mid
L3
AI crawler accessibility — GPTBot and ClaudeBot can access your site, but client-side rendered content on [X] pages is invisible to crawlers. Partially blocking indexation.
+5–10 pts
Weak
L4
Answer-first content structure — No direct-answer blocks on any service pages. Content with structured headings is 2.8× more likely to earn AI citations. Largest single content opportunity.
+12–20 pts
Mid
L5
Citation density & evidentiary depth — Some long-form content exists but lacks expert quotes and inline source citations. These two additions alone lift citation probability 41% and 30% respectively.
+10–18 pts
Weak
L6
Third-party authority building — 2 authoritative citations vs 12 for your top competitor. This is the heaviest lift and where the largest moats get built. Slow (3–6 months) but durable.
+15–25 pts
Weak
L7
Content freshness — [X] top pages not updated in 3+ months. Pages not updated within 2 months see citations drop sharply. Perplexity is especially punishing on stale content.
+5–12 pts
Weak
L8
Cross-platform monitoring — No current tracking across platforms. Citation volumes can differ by a factor of 615 across AI platforms. Without monitoring, optimization is guesswork.
Compounds others
Combined estimated lift: Executing Levers 1–6 fully moves your visibility score from the 15–30 pre-AEO baseline into the 50–70 range within 90 days. Levers 7–8 sustain and compound those gains over 6–12 months.
Brand entity health · Lever 2
How AI currently sees your business
LLMs build a knowledge graph of your business by aggregating signals across multiple sources. When your name, address, founding date, or core service description varies across sources, the AI gets confused about your identity — and cites you less confidently. We audited your presence across the sources that feed AI training and retrieval.
Entity consistency
[XX]/100
Business name, address, and key facts consistent on [X] of [Y] checked sources.
Schema implementation · Lever 1
[XX]/100
[X] of [Y] core schema types correctly implemented on key pages.
Authority signals · Lever 6
[XX]/100
Citations from third-party sources LLMs use for retrieval and training.
Wikipedia / Wikidata presence
[Yes/No]
Wikidata and Wikipedia carry disproportionate weight as authoritative anchor points for LLMs.
Specific issues identified
01
Business name spelled inconsistently across [X] sources. Most common cause of LLM identity confusion — and a 30-minute fix once sources are identified.
Critical
02
Organization schema missing on homepage. Standard JSON-LD implementation takes 30 minutes and immediately improves how AI models parse your core entity facts.
Quick win
03
FAQPage schema absent on service pages. FAQPage maps directly to question-answer queries — the dominant AI search format. Major opportunity on ChatGPT, Perplexity, and Grok.
Quick win
04
Address discrepancy between Google Business Profile and website. Update both to match. Inconsistency degrades local AI citation confidence across all platforms.
Quick win
90-day roadmap
What to fix, in what order, and what it's worth
A prioritized plan based on the 8-lever framework. The sequence matters: Foundation (Levers 1–3) is prerequisite work that every other lever depends on. Content Engine work (Levers 4–6) runs concurrently starting Day 31 — content and citation building both take 6–12 weeks to materialize, so they must start together. Advanced (Levers 7–8) activates at Day 90 to sustain and compound gains.
Current visibility score
[XX]/100
Typical pre-AEO baseline: 15–30
→
Projected at 90 days
[XX]/100
Typical post-AEO range: 50–70
01
Days 1–30 · Foundation
Fix entity consistency, schema, and crawler access
Levers: L1 Schema · L2 Entity consistency · L3 Crawler accessibility
Standardize your business name and core facts across all major directory sources. Implement Organization, LocalBusiness, FAQPage, and HowTo schema on key pages. Update Google Business Profile and Wikidata where eligible. Audit and fix crawler blocking — GPTBot, ClaudeBot, PerplexityBot. If the AI can't read your content, none of the subsequent levers matter.
Effort: LowVisibility lift: +19 to +36 pts combinedTime to impact: 2–8 weeks
02
Days 31–60 · Content engine + citation building (concurrent)
Answer-first content, evidentiary depth, and authority placements
Levers: L4 Answer-first content · L5 Citation density · L6 Third-party authority
Restructure high-priority pages with direct-answer blocks (40–60 words, neutral tone, at the top of every section). Publish 4–8 new long-form pieces with structured headings, 3–5 cited statistics, and 1–2 expert quotes per piece. Concurrently, begin earning third-party mentions on the publications LLMs trust — 4–6 guest placements, targeted PR, structured directory submissions. Content and citation work run in parallel because the citation curve takes time to respond regardless of when it starts.
Effort: HighVisibility lift: +37 to +63 pts combinedTime to impact: 4–12 weeks
03
Day 90 onward · Advanced — sustain and compound
Freshness cadence and cross-platform monitoring
Levers: L7 Freshness maintenance · L8 Cross-platform monitoring
Quarterly content refresh across your top 20 pages — new statistics, updated examples, refreshed schema dateModified properties. Weekly multi-platform monitoring across all five platforms using a fixed set of 50–100 buyer queries. Monthly full audits tracking citation rates and competitor share. Adapt content strategy based on what's actually getting cited. This is where single-tactic results compound into category-defining visibility.
Effort: Medium (ongoing)Visibility lift: Compounds all prior leversTime to impact: Continuous
On outcomes: Visibility scores typically move from the 15–30 baseline to the 50–70 range within 90 days when the 8 levers are executed well. The citation curve continues to compound for 6–12 months after that. AI platform citation behavior is not deterministic — platforms update their training and retrieval indexes on different schedules. What we promise is disciplined execution of the framework and a clear measurement system. Visibility outcomes follow when the work is done well.
Next steps
What happens now
You have everything you need to close your visibility gap. The choice is whether to execute in-house or with us. Either way, the gaps don't disappear on their own — and competitors extend their citation lead every month you wait.
Work with Citebound
We execute the full 8-lever framework end-to-end — multi-platform monitoring, monthly reporting tied to the metrics that actually predict revenue (citation rate, recommendation share, AI referral traffic), and guaranteed visibility score improvement of at least 15 points within 90 days, or your next month is free. Three retainer tiers based on depth and pace.
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Or execute in-house
The 8-lever framework and this roadmap are yours regardless. The Citebound 2026 AI Search Whitepaper has the full tactical detail behind every lever — including the Princeton GEO research, platform-specific citation behaviors, and implementation guidance. If you have a marketing team with technical SEO and content capability, allocate 20–30 hours per week for the first 90 days, then 10–15 hours per week for ongoing maintenance.
Next call scheduled
[Date and time]
Your point of contact
[Your name] · [email]
The AI search visibility agency for operators · citebound.com