A copy-paste Claude prompt that audits your brand's citations across ChatGPT, Claude.ai, Perplexity, and Gemini separately — then produces engine-specific optimization recommendations grounded in each engine's citation mechanics. Only 11% of cited domains overlap between ChatGPT and Perplexity. A combined visibility score hides which engines are actually working.
A B2B SaaS team's AI visibility tool reports 35% citation rate across the AI search landscape. Looks decent. The team focuses on content production, expecting incremental gains. Six months later, the rate is still 35%. Why? Because the 35% combined score was hiding that ChatGPT was at 8%, Perplexity was at 62%, Claude.ai was at 31%, Gemini was at 41% — and the optimization that would lift ChatGPT (third-party brand entity strength) is fundamentally different from what would lift Perplexity (structured Q&A content). The team kept producing content optimized for the wrong engine. The combined score never moved because each engine was being optimized either correctly (Perplexity) or with effort that produced no movement (ChatGPT). Per-engine audits surface the specific mechanism failing per engine; combined scores hide the diagnosis underneath an averaged number.
The deeper problem is that only 11% of cited domains overlap between ChatGPT and Perplexity (Averi, March 2026 analysis of 680M citations). Only 12% of sources match across ChatGPT + Perplexity + Google AI (Passionfruit, 15K queries). Citation volume variance across engines reaches 615× for the same brand on the same query. These are different platforms with different mechanics — ChatGPT relies primarily on parametric knowledge from training data; Perplexity retrieves real-time from 200B+ URLs; Claude.ai weighs site authority and explicit comparison structure; Gemini weighs Google ecosystem signals. The optimization playbook that works on one produces zero movement on the others.
This workflow runs structured per-engine analysis. Claude takes your prompt library + per-engine citation observations and produces a 4-engine diagnostic: per-engine citation rate, divergence analysis (where engines disagree), per-engine source pool analysis, root cause hypothesis (entity deficit, retrieval deficit, authority deficit, or freshness deficit), and per-engine optimization queue. Run quarterly aligned with Track 01; ad-hoc at major model releases (GPT-5, Claude 5, Gemini 3, etc.).
The gold variables — your brand, prompt library, per-engine citation observations — are the parts you edit. Manual collection takes 2-4 hours; automated tooling (Passionfruit, OtterlyAI, Profound) takes 15 minutes. Run quarterly aligned with the rest of Track 01.
entity_strength_deficit: weak third-party brand mentions; missing from Wikipedia, LinkedIn editorial, review platforms — primary issue for ChatGPT under-citation
- retrieval_structure_deficit: poor sub-question structure, lacking H2 hierarchy, no comparison tables — primary issue for Perplexity under-citation
- authority_signal_deficit: weak structural hierarchy, no explicit comparisons, low domain authority — primary issue for Claude.ai under-citation
- freshness_or_ecosystem_deficit: stale content, weak Google ecosystem signals, GSC indexing issues — primary issue for Gemini under-citation
2. Cross-engine divergence analysis:
- Identify prompts where engines diverge most dramatically (e.g. cited by Perplexity but absent in ChatGPT; cited by Claude.ai but missing from Gemini)
- Largest divergences = highest-leverage optimization targets
3. Per-engine optimization queue (3-5 actions per engine):
- Each action grounded in that engine's specific citation mechanics
- Specific implementation steps, not generic recommendations
- Estimated impact (citation rate lift in next quarter)
- Priority: P1 (highest under-citation engine) / P2 (mid-tier) / P3 (already strong)
Engine-specific optimization patterns// Apply these patterns when producing recommendations.
For ChatGPT under-citation (entity strength deficit):
- Audit Wikipedia presence — is brand mentioned in Wikipedia entries for the category? If no, target Wikipedia inclusion via 3rd-party sources.
- Audit LinkedIn editorial mentions — is brand mentioned in LinkedIn long-form content by category influencers?
- Audit review platforms — G2, Capterra, TrustRadius listings + reviews. ChatGPT pulls from these.
- Audit category co-occurrence — does your brand appear in editorial pieces alongside top category competitors?
- Don't recommend on-page schema or content rewrites — these have minimal ChatGPT impact.
For Perplexity under-citation (retrieval structure deficit):
- Restructure top 5 category pages with H2 headings for each sub-question.
- Add comparison tables to product pages (Perplexity weighs explicit table structure).
- Reduce paragraph length — answer-first paragraphs under 180 words per section.
- Audit content freshness — Perplexity favors recent dates; pages older than 12 months without refresh get deprioritized.
- Verify PerplexityBot in robots.txt — must allow crawling.
For Claude.ai under-citation (authority signal deficit):
- Restructure clear H1-H2-H3 hierarchy on key pages.
- Add explicit comparison tables (Claude.ai weighs structured comparisons heavily).
- Strengthen domain authority via earned link building.
- Audit citation source quality on outbound links — Claude.ai weighs source quality.
For Gemini / AI Mode under-citation (Google ecosystem deficit):
- Verify GSC indexing health — pages must be indexed by Google before Gemini can cite them.
- Audit Core Web Vitals — page speed + mobile-friendliness directly impact Gemini citation.
- Strengthen structured data (FAQPage, HowTo, Product schema).
- Build YouTube + Google Maps presence — Gemini routes 21% of citations to Google properties.
Output format1. Headline: per-engine citation rate summary, divergence severity, top engine for optimization priority.
2. Per-engine citation rate matrix: 4 engines × rate / vs avg / root cause / priority.
3. Cross-engine divergence comparison: 8-10 representative prompts with per-engine citation status (cited / mention / absent).
4. Per-engine optimization queue: 4 sections, each with 3-5 actions grounded in engine-specific mechanics.
5. Honest calibration:
- If ChatGPT citation rate < 5% but other engines > 25%, brand entity strength is the primary issue. On-page optimization won't fix this — needs 12-24 month brand entity building campaign.
- If Perplexity citation rate < 10%, content structure is the primary issue. Restructuring top 5 pages typically lifts Perplexity citation 15-25% within 60-90 days.
- If Gemini under-citation correlates with poor GSC indexing health, fix indexing first — no Gemini optimization will work on un-indexed pages.
- If divergence between engines is < 10pp, brand has consistent presence across the AI search landscape — focus on incremental optimization rather than gap-closing.
- For brands with < $5M ARR, ChatGPT optimization may not be cost-effective — entity strength building is expensive and slow. Focus on Perplexity + Claude.ai + Gemini first.
// Be specific in optimization recommendations. "Improve content structure" is generic. "Restructure /best-saas-marketing-agency page with 7 H2 sub-questions, add a 6-row comparison table, reduce average paragraph length from 240 to 160 words" is actionable.
// Don't conflate engines. Recommendations that work for ChatGPT (entity building) won't lift Perplexity. Recommendations that work for Perplexity (content structure) won't lift ChatGPT. Per-engine specificity is the whole point.
// Apply the 4-deficit framework explicitly. If under-citation root cause isn't clear, default to entity_strength_deficit for ChatGPT, retrieval_structure_deficit for Perplexity, authority_signal_deficit for Claude.ai, freshness_or_ecosystem_deficit for Gemini.Sample output for a hypothetical mid-market B2B SaaS brand with 40-prompt library run across 4 engines. Audit surfaces dramatic divergence: 7% ChatGPT (entity deficit), 31% Claude.ai, 48% Perplexity (well-optimized), 18% Gemini (indexing issues). P1 priority: ChatGPT entity strength building.
Analyzing 40-prompt library × 4 engines = 160 data points (240 with 1.5× sampling per engine for noise reduction). Running per-engine diagnostic across ChatGPT, Claude.ai, Perplexity, and Gemini.
Per-engine diagnostic complete. Combined visibility: 26%. Per-engine reality: ChatGPT 7% (DRAMATIC under-citation, entity strength deficit) / Claude.ai 31% (slightly above category average, structural strength) / Perplexity 48% (well-optimized, retrieval structure healthy) / Gemini 18% (under-citation, freshness + ecosystem deficit). Cross-engine divergence is 41 percentage points between ChatGPT and Perplexity — among the highest-divergence cases observed in B2B SaaS. P1 priority: ChatGPT entity strength building. Estimated 12-24 month campaign required for meaningful lift.
parametric_knowledge_gapstructural_strengthretrieval_strengthindexing_+_cwv_issuesFAQPage, HowTo, Product schema across 14 product pages. Gemini weighs structured data heavily for category recommendation queries.
+2-4pp
Run quarterly aligned with the rest of Track 01. Re-run after major model releases (GPT-5, Claude 5, Gemini 3, etc.) — citation behavior often shifts substantially with new model versions.
Mix of category questions, comparison queries, how-to queries, problem-statement queries. Citation Gap Finder workflow output is the best starting library — it's already buyer-validated. If running standalone, build from sales calls + recent customer interviews + competitor SEO research.
Run Citation Gap Finder →Manual collection: run each prompt 3-5 times per engine; record citation status (cited / mentioned / absent), top 3 cited URLs (yours), top 3 cited URLs (competitors). Automated tooling (Passionfruit, OtterlyAI, Profound, Averi) runs at scale and handles sampling automatically — recommended for ongoing audits. Single-run manual collection works for initial audit.
Workflow takes 22-28 minutes for 40-prompt × 4-engine analysis. Claude classifies per-engine root cause, identifies divergence, produces engine-specific optimization queue. Output is ready to hand to content + SEO + brand teams — different teams own different engine optimizations.
SEO team owns Gemini fixes (60-90 day impact — indexing, CWV, schema). Content team owns Perplexity + Claude.ai maintenance (incremental quarterly improvements). Brand team owns ChatGPT entity strength campaign (12-24 month horizon — Wikipedia inclusion, LinkedIn editorial, review platforms). Re-run audit quarterly to measure progression and re-prioritize.
Same 4-engine framework, different audit depth. Pick the variant that matches your AI search optimization maturity.
Initial audits need wider sampling and more rigorous noise reduction. Standard prompt assumes 3-5 samples per prompt; initial baseline runs 8-10 samples per prompt and excludes prompts with high run-to-run variance (likely engine instability rather than brand-specific signal).
Standard audit measures absolute citation rate. Competitor benchmark adds relative measurement: per engine, how does your brand rank vs top competitors? Useful for understanding category share-of-voice rather than absolute presence. Surfaces where competitors are dominating specific engines you're absent from.
Some B2B SaaS accounts have audiences concentrated on one specific engine — technical buyers heavily on Claude.ai, marketing buyers on Perplexity, executives on ChatGPT. Standard 4-engine audit dilutes attention. Single-engine deep-dive runs 80-100 prompts on one chosen engine for granular sub-category analysis.
Most B2B SaaS teams measure AI visibility on one platform and assume the number applies everywhere else. The truth is 89% of the citation landscape is invisible to single-engine measurement. Run per-engine audits quarterly. Surface which engine is failing for which reason. Build engine-specific optimization queues. Or have senior GrowthSpree operators run quarterly per-engine analysis across MCP-connected data — the same operating motion run across 300+ B2B SaaS accounts.