Weighted precision
75.7%
Published across public benchmark audits.
Research3-site benchmark study
AI Visibility Benchmark
We audited three leading SaaS platforms — Stripe, Ahrefs, and HubSpot — using the same deep-audit pipeline to measure their readiness for AI-generated search. The results reveal consistent patterns that affect how AI systems cite, recommend, and represent these brands.
Publishing note
These public benchmark pages and samples are publishing artifacts, not anonymized customer stories. They exist so buyers can inspect the output quality before more rollout proof is published.
Strict precision
59.4%
True positives only, without partial credit.
Reviewed findings
101
Visible benchmark denominator, not a hidden one.
False-positive share
7.9%
Published rather than buried in marketing copy.
Stripe
81
GEO Score out of 100
Read case study →Ahrefs
77
GEO Score out of 100
Read case study →HubSpot
76
GEO Score out of 100
Read case study →- Avg GEO Score
- 78
- Pages crawled
- 90
- Queries tested
- 30
- Signals computed
- 45
- Recommendations
- 9
Executive Summary
The average GEO Score across the three benchmarked sites was 78 out of 100. Stripe scored highest at 81, followed by Ahrefs at 77 and HubSpot at 76. All three sites demonstrated strong technical infrastructure and high AI citation frequency, but shared a critical gap: zero structured data markup.
None of the 90 crawled pages across all three sites contained JSON-LD structured data. This single gap zeroed four signals on every audit — schema_markup_accuracy, structured_data_completeness, entity_disambiguation, and knowledge_graph_alignment — making it the single highest-impact improvement opportunity identified by the pipeline.
Benchmark Results
Signal Comparison
Each audit computes 15 signals. The table below shows how each site scored on every signal, revealing consistent patterns across all three properties.
| Signal | Ahrefs | HubSpot | Stripe |
|---|---|---|---|
| ai_citation_frequency | 99.00 | 98.22 | 98.67 |
| answer_retrievability | 100.00 | 100.00 | 100.00 |
| canonical_hreflang_integrity | 100.00 | 100.00 | 100.00 |
| competitor_citation_gap | 100.00 | 100.00 | 100.00 |
| content_parse_confidence | 80.50 | 86.00 | 85.17 |
| core_web_vitals | 100.00 | 100.00 | 100.00 |
| entity_disambiguation | 0.00 | 0.00 | 0.00 |
| featured_snippet_coverage | 42.50 | 51.33 | 50.67 |
| internal_link_authority | 83.60 | 83.60 | 81.88 |
| knowledge_graph_alignment | 0.00 | 0.00 | 0.00 |
| metadata_clarity | 56.67 | 61.67 | 40.00 |
| mobile_render_quality | 83.00 | 93.00 | 83.00 |
| page_crawl_efficiency | 100.00 | 100.00 | 100.00 |
| schema_markup_accuracy | 0.00 | 0.00 | 0.00 |
| structured_data_completeness | 0.00 | 0.00 | 0.00 |
Key Findings
- Zero structured data across all three sites — none of the 90 crawled pages contained JSON-LD structured data
- Entity disambiguation universally absent — all three sites lack explicit entity definitions for AI extraction
- Knowledge graph alignment missing across all sites — no structured connections to knowledge graph entities
- Technical foundations strong everywhere — Core Web Vitals, crawl efficiency, and canonical integrity scored 100 on every audit
- AI citation frequency near-perfect across all brands — citation scores above 98 on every site
- Metadata clarity varies significantly — Stripe scored lowest (40) while HubSpot scored highest (61.67)
- Featured snippet coverage moderate everywhere — all sites scored between 42-51, indicating room for answer-ready formatting
What This Means for Your Company
Even leading SaaS brands show measurable gaps in AI visibility. Across the benchmark sites we observed missing structured data, weak entity definitions, and inconsistent metadata clarity. These issues reduce how often AI systems cite a site in generated answers.
The universal absence of JSON-LD structured data across all three major SaaS properties suggests these are common gaps in the industry, not edge cases. If industry leaders have these gaps, most sites likely do too — which means early adopters of GEO optimization have a meaningful competitive advantage.
The good news: the highest-impact improvements are within every site's control. Implementing structured data, fixing metadata, and adding entity definitions are concrete, implementable changes that directly address the zero-scoring signals.
Methodology
Each site was evaluated using the Citemeter deep audit pipeline, a five-stage process designed to measure AI visibility readiness:
- Crawl — Bounded same-origin crawl captures up to 30 pages, extracting HTML content, metadata, structured data, and internal link structure
- Screenshot — Headless browser captures 5 visual snapshots of key pages for mobile render quality assessment and visual evidence
- Analyse — LLM-driven analysis evaluates content quality, extractability, entity clarity, and structural patterns
- Query Test — 10 queries across 6 categories tested against simulated AI engines for brand mention, position, and sentiment
- Signal Computation — 15 signals computed from crawl, analysis, and query data, rolled up into 5 dimensions and overall GEO Score
All query results are generated through simulated AI-engine outputs. They reflect how an AI model interprets and responds to queries about the brand, not live results from ChatGPT, Perplexity, or Google AI Overviews. Signal scores are computed from the 30-page bounded crawl per site, not the full domain.
Individual Case Studies
Each site has a detailed case study with full signal breakdowns, dimension scores, recommendations, and evidence references.
Payments infrastructure
Stripe
81
88 authority · 30 pages crawled · $0.129 LLM cost
Read case study →SEO & marketing platform
Ahrefs
77
88 authority · 30 pages crawled · $0.098 LLM cost
Read case study →CRM & inbound marketing
HubSpot
76
85 authority · 30 pages crawled · $0.137 LLM cost
Read case study →Pipeline Reliability Notice
All 15 signals were computed with complete reliability state across all three audits. Query simulation uses AI-generated responses rather than live search engine results. Citation frequency, sentiment, and competitive positioning data reflect simulated AI behavior and should be interpreted as directional indicators, not definitive measurements of live AI search surfaces.
Each audit used a bounded 30-page crawl. These are large sites with thousands of pages — the crawled sample is representative but not exhaustive. All findings derive from pipeline outputs; no manual adjustments were applied.
See how your site scores
Run the same five-stage pipeline shown in this study on your own domain — score, evidence, and Fix Pack included.
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Looking for the narrative summary? Read the Benchmark Series Summary for aggregate metrics and buyer-facing context.