AI Visibility & SEO Report

Example Corp – https://www.example-corp.com

Feb 17, 2026 at 09:05 PM

AI Visibility Score

72
/
C
AI Ready badge

AI systems can find your content but struggle to cite it accurately.

7 passing, 7 warnings, 2 failing (16 checks total)

Generative Engine Optimization (GEO) Summary

Example Corp scores 72 out of 100 for AI search readiness, which means AI systems can find and crawl the site but struggle to extract clean, citable content from it. The strongest area is technical setup: all AI crawlers are allowed and schema markup is solid. The biggest gaps are in content structure, where deep HTML nesting, missing answer-first patterns, and paragraphs that depend on surrounding context reduce how often AI systems quote this content in their responses.

Technical Action Items

  • Create a /llms.txt file with site description, key pages, and content sections. This file is missing entirely and prevents AI models from discovering your preferred content. (technical.llms_txt, score 0%)
  • Reduce HTML nesting from 9 levels to 5 or fewer, and use semantic list and table elements. Deep div nesting creates noise when AI systems convert your page to markdown. (structural.markdown_fidelity, score 40%)
  • Rewrite section openings to lead with the main point, not background context. Only 3 of 6 sections lead with a direct answer. (structural.answer_first, score 50%)
  • Replace anaphoric references ('This,' 'That,' 'It') with specific nouns so each paragraph makes sense if quoted in isolation. (structural.passage_self_containment, score 70%)
  • Fix 2 heading level skips (H1 to H3, H3 to H5) and expand single-word headings into descriptive phrases. (structural.heading_hierarchy, score 60%)
  • Add missing Wikidata properties: official website, founding date, and industry classification. (entity.knowledge_graph, score 80%)

Full Report Findings

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Technical

78%
LLMs.txt
0% FAIL

No /llms.txt file found. AI models cannot discover your preferred content.

No /llms.txt file

Recommendation: Create a /llms.txt file so AI systems can understand your site at a glance. The specification is at llmstxt.org.

  • Start with a title line (# Your Brand Name) and a one-sentence description in a blockquote (> We help companies do X).
  • Add ## sections to organize links to your most important pages. For example: ## Products, ## Documentation, ## About.
Suggested Fixes
llms.txt: Complete template (llmstxt.org spec)
# Example Corp

> Example Corp helps companies improve their visibility in AI-generated search results.

## Key Pages

- [Home](https://www.example-corp.com/)
- [About](https://www.example-corp.com/about)
- [Blog](https://www.example-corp.com/blog)

## Documentation

- [Getting Started](https://www.example-corp.com/docs/getting-started)
- [API Reference](https://www.example-corp.com/docs/api)

# Reference: https://llmstxt.org

Update the page URLs and description to match your actual site. Save this file at the root of your domain as /llms.txt.

Technical Details
llms_txt_present:
False (Whether a /llms.txt file was found)
llms_full_txt_present:
False (Whether a /llms-full.txt file was found)
Content Freshness
70% WARNING

Content was last modified 45 days ago.

Last modified 45 days ago (source: http header)

Recommendation: This content has not been updated in over 6 months. Review the page for accuracy, make any needed updates, then set dateModified to today's date.

  • Add a dateModified field so AI systems know when this page was last updated. In your JSON-LD markup, include: "dateModified": "2024-11-15".
Technical Details
days_since_modified:
45 (Days since the page was last modified)
source:
http_header (Where the date data was found: schema, HTML meta, sitemap, or HTTP header)
JavaScript Rendering Gap
82% PASS

Minimal rendering gap. Content is accessible without JavaScript.

12.0% content requires JavaScript (880 vs 1000 words)

Recommendation: Add a <noscript> tag with fallback content. This gives AI crawlers and users with JavaScript disabled a summary of the page.

Technical Details
gap_percentage:
12.0 (% of content only visible after JavaScript runs)
raw_word_count:
880 (Words visible in raw HTML without JavaScript)
rendered_word_count:
1000 (Words visible after browser executes JavaScript)
word_count_ratio:
0.88 (Raw / rendered word ratio; 1.0 = no JS dependency)
spa_detected:
False (Whether the page uses an empty SPA shell container)
spa_framework:
None (Detected JS framework: Next.js, Nuxt, React, Vue, or Angular)
Schema Markup
85% PASS

4 of 5 schema categories present. Missing: FAQ Schema.

Found: Article, BreadcrumbList, Organization

Recommendation: If this page has Q&A content, wrap it in FAQPage schema. AI systems heavily cite FAQ markup because each question-answer pair is a self-contained, quotable passage. You already have Article, BreadcrumbList, Organization markup.

Technical Details
json_ld_block_count:
3 (Number of JSON-LD script blocks on the page)
total_types_found:
3 (Number of distinct schema.org types detected)
uses_graph_format:
False (Whether JSON-LD uses the @graph format)
AI Crawler Access
90% PASS

All 12 AI crawlers are allowed access.

All 12 AI crawlers allowed

Recommendation: All AI crawlers are allowed. Keep monitoring for new crawler user agents as more AI companies launch their own bots.

Technical Details
allowed_count:
12 (Number of AI crawlers allowed by robots.txt)
blocked_count:
0 (Number of AI crawlers blocked by robots.txt)
total_bots:
12 (Total AI crawlers checked)
robots_txt_present:
True (Whether a robots.txt file was found)
has_wildcard_block:
False (Whether a wildcard User-agent: * Disallow rule exists)
weighted_score:
1.0 (Weighted access score across all AI crawlers)

Structural

68%
Markdown Fidelity
40% FAIL

Markdown conversion issues: significant content lost or noise added; deep div nesting (9 levels).

Only 38% of content preserved max nesting: 9 levels

Recommendation: Your HTML has 9 levels of nested <div> tags. Reduce this to 5 or fewer. Deep nesting creates noise when AI systems convert your page to Markdown for processing.

  • Use <ul>/<ol> with <li> elements for lists instead of styled <div> tags. Proper HTML lists convert to Markdown bullet and number lists automatically.
  • Use standard HTML <table> markup (with <thead>, <tbody>, <tr>, <th>, <td>) for tabular data. CSS grid or div-based table layouts do not convert to Markdown tables, so AI systems lose the data structure.
  • Some content appears to be embedded in non-text elements (background images, CSS-only visibility, or icon fonts). This content disappears when AI systems convert the page to Markdown. Move it into visible HTML text.
Technical Details
markdown_length:
1820 (Character length of converted Markdown output)
source_text_length:
4750 (Character length of visible text in HTML source)
content_ratio:
0.38 (Markdown text / source text ratio; 1.0 = no content loss)
max_div_nesting:
9 (Deepest level of nested div tags in HTML)
source_has_headings:
True (Whether the HTML contains heading tags)
md_heading_count:
3 (Number of # heading markers in Markdown output)
source_has_lists:
True (Whether the HTML contains list elements)
md_list_markers:
1 (Number of list markers in Markdown output)
source_has_tables:
True (Whether the HTML contains data tables)
md_table_pipes:
0 (Number of table pipe rows in Markdown output)
Answer First
50% WARNING

3 of 6 sections lead with concrete answers.

3 of 6 sections lead with concrete answers

Recommendation: Start each section with its main point, not background context. AI systems quote the first sentence, so make it count.

  • Move setup language and definitions after the answer. Use the inverted pyramid: conclusion first, evidence second.
Technical Details
strong_sections:
3 (Sections scoring 0.8+ for answer-first quality)
section_count:
6 (Total heading+paragraph sections analyzed)
Heading Hierarchy
60% WARNING

Heading structure issues: 2 heading level skip(s).

8 headings, 2 level skips

Recommendation: Fix 2 heading level skips: H1 -> H3, H3 -> H5. Headings should follow in order (H1, then H2, then H3). Skipping levels confuses AI systems when they parse document structure.

  • 3 headings are just single words (e.g., 'Overview'). Expand them into descriptive phrases that tell readers and AI systems what the section covers.
Technical Details
heading_count:
8 (Total heading tags found on the page)
h1_count:
1 (Number of H1 tags (should be exactly 1))
h2_count:
2 (Number of H2 subheading tags)
descriptive_ratio:
0.625 (Fraction of headings that are descriptive phrases or questions)
empty_headings:
0 (Heading tags with no text content)
too_short_count:
3 (Single-word headings that lack descriptive detail)
too_long_count:
0 (Headings exceeding 15 words)
Passage Self-Containment
70% WARNING

12 paragraphs analyzed, 3 scored below threshold.

12 paragraphs, 3 scored below threshold

Recommendation: Several paragraphs start sentences with 'This,' 'That,' or 'It,' which makes them meaningless without context. Replace these with the specific noun they refer to.

  • Each paragraph should make sense on its own if an AI quotes it without the surrounding text. Add names, numbers, or proper nouns to anchor each passage.
Technical Details
paragraph_count:
12 (Total paragraphs analyzed on the page)
low_scoring_count:
3 (Paragraphs scoring below 0.5 for citability)
Fact Density
85% PASS

Good fact density: 4.2 facts per 100 words.

4.2 facts per 100 words (42 total)

Recommendation: Your fact density is strong. Keep including specific numbers, dates, and named entities throughout your content.

  • Add source attributions or direct quotes. For example: 'According to Gartner, 65% of enterprises will adopt AI by 2025.' Sourced claims are more likely to be cited by AI systems.
Technical Details
facts_per_100_words:
4.2 (Fact density normalized to 100 words)
total_facts:
42 (Total verifiable facts found across all categories)

Entity

65%
AI Brand Check
54% WARNING

5 of 8 AI providers recognize 'Example Corp'. Some providers lack information about this brand.

5 of 8 AI providers recognize this brand
AI Provider Responses
ChatGPT (gpt-4o-mini) Recognized
Example Corp is a B2B SaaS company founded in 2015 that provides marketing analytics tools for mid-size businesses. Their flagship product is a dashboard that integrates with major advertising platforms to provide unified campaign reporting.
Industry: Yes Founded: 2015 Products: Yes Detail: Rich
Claude (claude-sonnet-4-5-20250929) Recognized
Example Corp is a marketing technology company that offers analytics and reporting solutions for small and medium businesses. Founded in 2015, the company is known for its data integration platform that connects multiple advertising channels into a single dashboard.
Industry: Yes Founded: 2015 Products: Yes Detail: Rich
Gemini (gemini-2.0-flash) Recognized
Example Corp is a software company specializing in marketing analytics and data-driven advertising solutions. Founded in 2015, the company serves businesses looking to consolidate their advertising data from multiple platforms into actionable insights.
Industry: Yes Founded: 2015 Products: Yes Detail: Rich
Perplexity (perplexity/sonar) Recognized
Example Corp is a marketing technology company founded in 2015 that provides analytics software for advertising teams. The platform integrates with Google Ads, Meta Ads, and LinkedIn Campaign Manager to provide unified reporting dashboards.
Industry: Yes Founded: 2015 Products: Yes Detail: Rich
Mistral (mistral-small-latest) Recognized
Example Corp is a technology company that provides marketing and analytics software solutions. The company helps businesses track and optimize their digital advertising campaigns across multiple channels.
Industry: Yes Products: Yes Detail: Rich
DeepSeek (deepseek-chat) Not recognized
I don't have specific information about a company called Example Corp. It does not appear in my training data as a widely recognized brand.
Qwen (qwen-turbo) Not recognized
I'm not familiar with a company called Example Corp. I don't have reliable information about this brand.
Solar (solar-mini) Not recognized
I don't have information about this brand. Example Corp does not appear to be a widely recognized company in my available data.

Recommendation: Not all AI systems recognize your brand. Build your presence on Wikipedia, Wikidata, and Crunchbase. AI models learn from these sources during training, so having profiles there increases the chance they know who you are.

  • Keep your brand description consistent across your website, social media, and third-party profiles. When every source says the same thing, AI models converge on an accurate answer.
  • State key facts clearly on your About page: what you do, when you were founded, and your main products. Add Organization schema markup with these details.
Technical Details
brand_name:
Example Corp (Brand name queried across AI providers)
providers_queried:
8 (Number of AI providers asked about the brand)
providers_responded:
8 (Providers that returned a valid response)
providers_recognized:
5 (Providers that recognized the brand)
recognition_rate:
0.625 (Fraction of responding providers that know the brand)
consistency_score:
0.833 (Agreement level across provider descriptions)
richness_score:
0.6 (Average detail level in provider descriptions)
Knowledge Graph
80% PASS

Wikidata entity found with moderate property coverage.

Entity Q12345678 with 12/20 properties

Recommendation: Add missing properties to your Wikidata entry: official website URL, founding date, and industry classification. These help AI systems answer questions about your brand accurately.

  • Review your Wikidata entry periodically. Outdated information can cause AI systems to give wrong answers about your brand.
Technical Details
entity_found:
True (Whether a Wikidata entity was found)
entity_id:
Q12345678 (Wikidata entity identifier (e.g. Q12345))
property_coverage:
12/20 (Key properties found vs. recommended total)

SEO Health Check

74/100

Search Engine Optimization (SEO) Summary

The SEO health check scores 74 out of 100. Core meta tags and social sharing markup are in good shape, but image optimization and page speed signals need attention. Fixing these will improve both traditional search rankings and AI crawl efficiency.

Technical Action Items

  • Add width and height attributes to all images and convert to WebP format. 3 of 8 images are missing dimensions, causing layout shift. (seo.image_optimization, score 60%)
  • Enable text compression (gzip/brotli) and set Cache-Control headers for static assets. Current TTFB is acceptable but transfer sizes are larger than needed. (seo.page_speed_signals, score 55%)
  • Add descriptive alt text to 2 images that currently have empty alt attributes. (seo.image_optimization, score 60%)

SEO

74%
Page Speed Signals
55% WARNING

Transfer size is 2.1 MB. Text compression not detected. TTFB is 380ms.

0KB HTML No compression

Recommendation: Enable gzip or brotli compression for HTML, CSS, and JS files. This can reduce transfer size by 60-80%.

  • Set Cache-Control headers for static assets (images, CSS, JS) with a max-age of at least 1 week.
Technical Details
transfer_size_bytes:
2200000 (Total page transfer size in bytes)
ttfb_ms:
380 (Time to first byte in milliseconds)
text_compression:
False (Whether gzip or brotli compression is enabled)
cache_control_present:
False (Whether Cache-Control headers are set)
Image Optimization
60% WARNING

3 of 8 images missing width/height attributes. 2 images have empty alt text.

0/8 images have alt text

Recommendation: Add explicit width and height attributes to all images to prevent Cumulative Layout Shift (CLS). 3 images are missing these attributes.

  • Write descriptive alt text for the 2 images that have empty alt attributes. Alt text helps both search engines and screen readers.
  • Convert the 1 oversized image to WebP format and resize to the display dimensions.
Suggested Fixes
HTML: Image tag template (3 image(s) missing width/height, 2 image(s) missing alt text)
<img
  src="image.jpg"
  alt="TODO: Describe this image for screen readers and search engines"
  width="800"
  height="600"
  loading="lazy"
>

Apply width, height, alt, and loading='lazy' attributes to each image on your page. Use the actual image dimensions.

Technical Details
total_images:
8 (Total img tags found on the page)
missing_dimensions:
3 (Images missing width and height attributes)
missing_alt:
2 (Images missing alt text)
oversized_images:
1 (Images larger than their display size)
Link Health
80% PASS

45 links checked. No broken links found. 3 external links missing rel=noopener.

45 links (0 internal)

Recommendation: Add rel='noopener' to the 3 external links that open in new tabs. This prevents the opened page from accessing your window object.

Technical Details
total_links:
45 (Total anchor tags found on the page)
broken_links:
0 (Links with broken or empty href values)
external_links:
12 (Links pointing to other domains)
missing_noopener:
3 (External links missing rel=noopener)
Meta Tags
85% PASS

Title, description, and canonical URL are present and well-formed.

Missing title tag Missing meta description

Recommendation: Meta tags are in good shape. Consider A/B testing title variations to improve click-through rate from search results.

Technical Details
title_present:
True (Whether an HTML title tag exists)
title_length:
52 (Character length of the title tag)
description_present:
True (Whether a meta description tag exists)
description_length:
148 (Character length of the meta description)
canonical_present:
True (Whether a canonical URL link tag exists)
viewport_present:
True (Whether a viewport meta tag exists)