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datasembly.com

datasembly.com

Moderate AI visibility with 28 of 48 criteria passing. Biggest gap: llms.txt file.

60/100
4 since v1
D
Citation Avg
Answer Readiness
~40% weight
7/10
Content Structure
~25% weight
6/10
Trust & Authority
~15% weight
8/10
Technical Foundation
~10% weight
4/10
AI Discovery
~10% weight
5/10

Verdict

Below-average AEO readiness at 60/100 - multiple areas need attention. Key strengths include Entity Authority & NAP Consistency, Internal Linking Structure, and Sitemap Completeness. Priority gaps: llms.txt File, RSS/Atom Feed, and Content Licensing & AI Permissions.

#819of 13363
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How to Improve

Current
60/100
Projected
84/100
36Total Fixes
13Quick Wins
~63hEst. 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 signalsExplain how content was tested or reviewedImprove citation-ready writing qualityAdd answer-first content placementClarify entity boundariesReduce extraction friction
Create /llms.txt file
critical|low
Add llms-full.txt with extended content
medium|low
Reduce render-blocking resources
critical|medium
Configure robots.txt for AI crawlers
high|trivial
Fix HTML structure and meta tags
low|low
Add internationalization signals
critical|medium
Reduce document weight
medium|low
Improve server response efficiency
medium|low

Top Opportunities10

Ensure blog content consistently covers your core expertise areas rather than scattering across unrelated topics. AI engines build authority models - a site about "Medicare coverage" that also publishes about humidifiers and groceries dilutes its topical authority.

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

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

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

Add inline citations to external sources, "According to [Source]..." attribution phrases, and a Sources section at the end of key articles.

Write self-contained definition sentences and single-claim statements that AI engines can quote directly. Avoid pronouns like "this" or "that" at the start of answer paragraphs.

Write 20-25 word self-contained answer sentences immediately after each H2 heading. 72.4% of AI-cited posts use this pattern - it gives engines a ready-made snippet to quote.

Show direct use, testing, implementation, or lived experience with concrete observations, examples, screenshots, and lessons learned.

Place a concise 40-80 word answer block in the first 300 words of each page. Avoid throat-clearing openers like "In this article..." and lead with the answer.

Define the primary entity in the first 500 characters, use consistent terminology (same term 70%+), and add "unlike X" signals to help AI engines distinguish your topics.

Fix It With AI39

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Site Pages

datasembly.com/newsF34
News Archive | Datasembly
datasembly.com/blogsF38
Real-time, local data for pricing and promotions | Datasembly | Blogs
datasembly.com/how-much-will-back-to-school-cost-this-yearD43
How Much Will Back to School Cost This Year? | Datasembly
datasembly.com/cpg-analytics/pricing-and-analyticsF39
Competitive Retail Data for CPG Pricing & Analytics | Datasembly
datasembly.com/how-cpgs-are-using-datasembly-today-insigh...D43
How CPGs Are Using Datasembly Today: Insights from the POI Panel | Datasembly
datasembly.com/cpg-analytics/revenue-growth-managersD40
Competitive Data for Revenue Growth Managers | Datasembly
datasembly.com/retail-analytics/pricing-and-analytics-man...D40
Competitive Data for Retail Pricing & Analytics | Datasembly
datasembly.com/retail-analytics/merchandisersD40
Competitive Data for Retailer Merchandisers | Datasembly
datasembly.com/marketplace-data-technology/data-collectionD41
Full-Category Data Collection for CPG & Retail | Datasembly
datasembly.com/grocery-store-price-index-our-latest-resourceD43
Grocery Store Price Index: Our latest resource | Datasembly
datasembly.com/snapshot-grocery-price-intelligence/?indus...F35
Snapshot - Free Grocery Price Intelligence Preview | Datasembly
datasembly.com/marketplace-data-technology/competitive-pr...F37
Competitive Product Matching for Retail Intelligence | Datasembly
datasembly.com/trade-promotion-optimizationF39
Trade Promotion Optimization Tools for CPGs | Datasembly
datasembly.com/cpg-guide-to-grocery-retail-dataF35
CPG Guide to Grocery Data | Datasembly
datasembly.com/case-studiesD43
Real-time data for pricing and promotions | Datasembly | Case Studies
datasembly.com/resourcesD43
Real-time, local data for pricing | Datasembly | Resources
datasembly.com/request-a-demoF39
Hyper-local pricing and promotions data | Datasembly | Request a Demo
Changes since v1Last audited 63 days ago
56
60
+4
View full comparison →
Focus Content on Core TopicsAdd Direct Answer ParagraphsRestructure Content as Q&AImprove Question-Answer AlignmentPackage Evidence for AIImprove Citation-Ready WritingAdd Answer Capsule PatternsAdd First-Hand Experience SignalsAdd Answer-First PlacementAdd Entity Disambiguation

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