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thinknum.com

thinknum.com

Moderate AI visibility with 26 of 48 criteria passing. Biggest gap: llms.txt file.

50/100
F
Citation Avg
Answer Readiness
~40% weight
7/10
Content Structure
~25% weight
4/10
Trust & Authority
~15% weight
5/10
Technical Foundation
~10% weight
4/10
AI Discovery
~10% weight
5/10

Verdict

Below-average AEO readiness at 50/100 - multiple areas need attention. Key strengths include Sitemap Completeness, Canonical URL Strategy, and Entity Density. Priority gaps: llms.txt File, Comprehensive FAQ Section, and RSS/Atom Feed.

#5427of 13363
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How to Improve

Current
50/100
Projected
80/100
43Total Fixes
10Quick Wins
~82hEst. Effort
Quick Wins
Create /llms.txt fileAdd question-based headingsConfigure robots.txt for AI crawlersImplement semantic HTML5 elementsAdd definition-style contentAdd ai.txt and content licensingImprove query-answer alignmentAdd visible date signalsIncrease content depth and structureImprove server response efficiency
Create /llms.txt file
critical|low
Add llms-full.txt with extended content
medium|low
Configure robots.txt for AI crawlers
high|trivial
Fix HTML structure and meta tags
low|low
Add internationalization signals
critical|medium
Improve server response efficiency
high|low
Reduce render-blocking resources
low|low
Reduce document weight
low|low

Top Opportunities10

Write concise, standalone answer paragraphs (2-3 sentences) immediately after question headings. These "snippet-ready" paragraphs are ideal for AI engine citations.

Use HTML tables for comparison data and ordered/unordered lists for features, steps, and specifications. Structured data formats are directly extractable by AI engines for answers.

Expand articles to 1000+ words with structured H2/H3 sections, comparison tables, and expert analysis. Thin content (under 300 words) is rarely cited by AI engines. Deep, well-structured articles demonstrate expertise.

Sections within pages contain identical or near-identical text. LLMs may flag this as low-quality or thin content, reducing citation authority. Rewrite duplicate blocks with unique angles.

Create a dedicated FAQ page with FAQPage schema markup. Cover common questions about your products, services, and industry to become a direct answer source for AI engines.

Add question-based headings (H2/H3) throughout your content. Use "What is...", "How does...", "Why should..." patterns that match how users query AI assistants.

Add specific numbers, percentages, statistics, and data points throughout your content. Fact-dense content gives AI engines concrete data to cite rather than vague claims.

Ensure every question-format heading (H2/H3) is followed by a direct answer paragraph. This pattern is ideal for AI engine snippet extraction.

Include dateModified schema, visible last-updated dates, and time elements on content pages. Fresh content signals help AI engines prioritize your pages over stale alternatives.

Rewrite multi-clause sentences into single-claim statements under 20 words. Pages with Flesch-Kincaid grade 16 outperform grade 19 in citation rates.

Fix It With AI45

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Site Pages

www.thinknum.com/resourcesF35
Thinknum Alternative Data – Wield the web as data
www.thinknum.com/datasets/propertyD43
Properties | Thinknum Alternative Data – Wield the web as data
www.thinknum.com/referralF32
Thinknum Alternative Data – Wield the web as data

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