Live Platform Vertical Data
Verticals are inferred from domain name patterns. Data is anonymized aggregate — no brand names or PII. Refreshes hourly.
| Vertical | Scans | Avg AIS Score | % Below 40 | vs. Baseline |
|---|
Data source: AISearchStackHub proprietary scan database. Vertical classification via domain regex patterns. AIS Index formula: V*0.40 + A*0.30 + S*0.20 + Ad*0.10. Refreshed hourly. JSON endpoint: /api/research/verticals.
Research Baseline: 527-Brand Study
From our January-April 2026 curated study of 527 brands, selected to be representative of each vertical. The live platform data above reflects organic scan users; this baseline uses a controlled, stratified sample.
| Vertical | Sample Size | Avg AIS Score | Top Quartile Threshold | % Scoring Below 20 |
|---|---|---|---|---|
| B2B SaaS | 147 | 38 / 100 | 61+ | 19% |
| Fintech | 118 | 33 / 100 | 56+ | 27% |
| Healthcare / Healthtech | 120 | 29 / 100 | 52+ | 34% |
| Ecommerce | 142 | 24 / 100 | 47+ | 53% |
Source: AISearchStackHub curated research study, Jan-Apr 2026. Full methodology at /state-of-ai-search-report.
Vertical Analysis: Why Scores Differ
B2B SaaS / Tech
B2B SaaS brands have a structural content advantage: the nature of the product requires them to publish technical documentation, API references, integration guides, use case libraries, and comparison content. This is exactly what LLMs learn to cite — structured, factual, reference-quality content that answers specific questions.
The gap between the best and worst SaaS brands is large. Top-quartile SaaS brands score above 61/100; bottom-quartile scores average 18/100. The difference comes down to transparency: brands that publish exact pricing, real customer case studies with quantified outcomes, and honest comparison content score dramatically higher.
- + Technical documentation and API reference pages
- + Case studies with quantified outcomes ("reduces churn by 34%")
- + Honest competitor comparison pages
- + Transparent pricing with exact figures
- + G2 / Capterra / Trustpilot review presence
Public context: B2B software market valued at $700B+ in 2025 (Gartner IT Market Forecast, Oct 2025). AI-adjacent SaaS tools are among the fastest-growing sub-categories with highest LLM query volume.
Fintech
Fintech brands benefit from high-frequency LLM query volume — users frequently ask AI for product comparisons, fee breakdowns, and financial tool recommendations. However, LLMs apply elevated caution in financial contexts, hedging recommendations and directing users toward professional advisors, which suppresses Authority scores.
Brands that establish regulatory credibility explicitly — publishing FDIC/SEC/state licensing information — see Authority scores 11 points above vertical average. Fee transparency pages with specific numeric data outperform vague "competitive rates" language consistently.
- + Explicit regulatory disclosures and certifications
- + Fee comparison tables with specific numbers
- + User outcome data (approval rates, avg savings)
- + Side-by-side competitor comparisons
- + Press coverage in financial publications
Public context: Global fintech market reached $340B in 2024, growing ~25% YoY (Statista, Jan 2026). AI-powered financial planning tools are among the top-queried product categories in ChatGPT and Perplexity.
Healthcare / Healthtech
Healthcare brands face the strongest YMYL (Your Money or Your Life) caution from all four LLMs. Direct product recommendations are routinely hedged with "consult a healthcare provider" language. However, Visibility scores are less affected — brands appear frequently in educational roles.
Top-quartile healthcare brands repositioned content strategy around educational authority rather than direct-to-consumer promotion: clinical study summaries, named medical advisory boards, and condition-level educational content scored 23 points above vertical average.
- + Clinical study summaries and peer-review citations
- + Medical advisory board with named, credentialed advisors
- + Condition-level educational content (not product promotion)
- + FDA clearance and regulatory status pages
- + Published outcomes data (clinical trial results, user studies)
Public context: Digital health market projected at $660B by 2025 (Statista, Dec 2025). LLM query volume for health-adjacent topics is among the highest of any category — but also subject to the most aggressive safety filtering.
Ecommerce / Retail
Ecommerce brands are the weakest performers in our dataset. The problem is not content volume — most publish substantial content — but content type. Product descriptions, promotional copy, and conversion-optimized pages are not formats that LLMs treat as authoritative sources in response to research or recommendation queries.
Outperformers publish research-quality content about their product category rather than about their products. A supplement brand publishing ingredient research. A furniture brand publishing sustainable materials guides. This content anchors LLM associations between the brand and domain authority.
- + Category research content (ingredient science, material sourcing)
- + Third-party review platform presence (Trustpilot, Reviews.io)
- + Sustainability and supply chain transparency pages
- + Buyer guides with honest category comparisons
- + Press coverage in category publications
Public context: Global e-commerce at $6.3T in 2024 (Statista, Mar 2026). Despite largest advertising spend of any vertical, ecommerce brands significantly underinvest in the content formats that drive LLM citation.
Methodology: Vertical Classification
Vertical classification in the live platform data is performed via regular expression matching against the domain name. Classification is approximate — no manual verification is performed on live platform data. The research baseline used manual vertical assignment by a researcher.
Domains not matching any pattern are classified as "Other." Vertical data API: /api/research/verticals (JSON, no auth, CC BY 4.0).
About This Dataset
Produced by AISearchStackHub using automated LLM visibility scanning. The live data reflects anonymous aggregate statistics from platform users. The research baseline is a controlled study from January-April 2026.
Data available under CC BY 4.0. Attribution: AISearchStackHub (aisearchstackhub.ai). Full methodology: /state-of-ai-search-report.